In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
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"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data Found celeba Data
show_n_images = 25
"""
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"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
<matplotlib.image.AxesImage at 0x7f5e14c60c18>
# import matplotlib
# matplotlib.__version__
# !pip install -U matplotlib==2.0.2
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.
show_n_images = 25
"""
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"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
<matplotlib.image.AxesImage at 0x7f5e14b58208>
\\\\\\\\\\\\\\\\\\\\\\## Preprocess the Data Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.
The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).
You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputsdiscriminatorgeneratormodel_lossmodel_opttrainThis will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
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"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
import tensorflow.contrib.slim as slim
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
/home/jsingh/anaconda3/envs/cv3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. from ._conv import register_converters as _register_converters
WARNING:tensorflow:From /home/jsingh/anaconda3/envs/cv3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version. Instructions for updating: Use the retry module or similar alternatives. TensorFlow Version: 1.7.0 Default GPU Device: /device:GPU:0
Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width, image_height, and image_channels.z_dim.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
input_real = tf.placeholder(tf.float32,[None,image_width,image_height,image_channels])
input_z = tf.placeholder(tf.float32,[None,z_dim])
learning_rate = tf.placeholder(tf.float32)
return input_real,input_z,learning_rate
"""
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"""
tests.test_model_inputs(model_inputs)
Tests Passed
Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False,alpha=0.2):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
with tf.variable_scope("discriminator",reuse=reuse):
# input_shape : (32,32,3)
net = slim.conv2d(images,64,5,stride=2,activation_fn=None) # (32,32,16)
net = tf.maximum(alpha*net,net)
net = slim.conv2d(net,128,5,stride=2,activation_fn=None)
net = slim.batch_norm(net)
net = tf.maximum(alpha*net,net)
net = slim.conv2d(net,256,5,stride=2,activation_fn=None)
net = slim.batch_norm(net)
net = tf.maximum(alpha*net,net)
net = slim.flatten(net)
logits = slim.fully_connected(net,1,activation_fn=None)
out = tf.sigmoid(logits)
return out,logits
"""
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"""
tests.test_discriminator(discriminator, tf)
Tests Passed
Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.
def generator(z,out_channel_dim,is_train=True,alpha=.2):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
with tf.variable_scope("generator",reuse=not is_train):
net = slim.fully_connected(z,4*4*512,activation_fn=None)
net = tf.reshape(net,[-1,4,4,512])
net = slim.batch_norm(net,is_training=is_train)
net = tf.maximum(alpha*net,net) # (4,4,)
net = tf.layers.conv2d_transpose(net,256,5,strides=2)
net = slim.batch_norm(net,is_training=is_train)
net = tf.maximum(alpha*net,net) # (8,8,)
net = tf.layers.conv2d_transpose(net,128,5,strides=2)
net = slim.batch_norm(net,is_training=is_train)
net = tf.maximum(alpha*net,net) # (16,16,)
net = tf.layers.conv2d_transpose(net,out_channel_dim,5,strides=2) #(32,32,output_channel_dim)
net = tf.image.resize_images(net,[28,28]) # (28,28,C)
out = .5*tf.tanh(net)
return out
"""
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"""
tests.test_generator(generator, tf)
Tests Passed
Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)generator(z, out_channel_dim, is_train=True)def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
gen_out = generator(input_z,out_channel_dim,is_train=True)
d_out_real,d_logits_real = discriminator(input_real,reuse=False)
d_out_fake,d_logits_fake = discriminator(gen_out,reuse=True)
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_logits_real)
,logits=d_logits_real))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_logits_fake)
,logits=d_logits_fake))
d_loss = d_loss_real+d_loss_fake
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_logits_fake)
,logits=d_logits_fake))
return d_loss, g_loss
"""
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"""
tests.test_model_loss(model_loss)
Tests Passed
Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
# Get weights and bias to update
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
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"""
tests.test_model_opt(model_opt, tf)
Tests Passed
"""
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"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)model_loss(input_real, input_z, out_channel_dim)model_opt(d_loss, g_loss, learning_rate, beta1)Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# print parameters
DISPLAY_STEP = 100
GEN_DISPLAY_STEP = 10*DISPLAY_STEP
# TODO: Build Model
step = 0
im_width , im_height , im_channels = data_shape[1:]
input_real,input_z,lr = model_inputs(im_width,im_height,im_channels,z_dim)
d_loss,g_loss = model_loss(input_real,input_z,im_channels)
d_opt,g_opt = model_opt(d_loss,g_loss,lr,beta1)
with tf.Session() as sess:
sess.run([tf.global_variables_initializer(),tf.local_variables_initializer()])
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
step += 1
# TODO: Train Model
# getting batch data
batch_real = batch_images
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# training the model
_,d_l = sess.run([d_opt,d_loss],feed_dict={input_real:batch_real,input_z:batch_z,
lr:learning_rate})
_,g_l = sess.run([g_opt,g_loss],feed_dict={input_real:batch_real,input_z:batch_z
,lr:learning_rate})
if step % DISPLAY_STEP == 0 :
print ("Epoch : {}/{} ... Step : {} ... d_loss : {} ... g_loss : {}"
.format(epoch_i+1,epoch_count,step,d_l,g_l))
if step % GEN_DISPLAY_STEP == 0 :
n_images = 25
show_generator_output(sess,n_images,input_z,im_channels,data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 100
learning_rate = 3e-3
beta1 = .5
"""
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"""
epochs = 10
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Epoch : 1/10 ... Step : 10 ... d_loss : 0.9336976408958435 ... g_loss : 7.311212539672852 Epoch : 1/10 ... Step : 20 ... d_loss : 0.3639373183250427 ... g_loss : 3.2976484298706055 Epoch : 1/10 ... Step : 30 ... d_loss : 0.17301833629608154 ... g_loss : 4.143669128417969 Epoch : 1/10 ... Step : 40 ... d_loss : 0.013400103896856308 ... g_loss : 6.166452407836914 Epoch : 1/10 ... Step : 50 ... d_loss : 0.029078524559736252 ... g_loss : 7.749451637268066 Epoch : 1/10 ... Step : 60 ... d_loss : 1.6513876914978027 ... g_loss : 9.911873817443848 Epoch : 1/10 ... Step : 70 ... d_loss : 0.1284271776676178 ... g_loss : 12.684028625488281 Epoch : 1/10 ... Step : 80 ... d_loss : 7.0270915031433105 ... g_loss : 4.399494647979736 Epoch : 1/10 ... Step : 90 ... d_loss : 0.1814710795879364 ... g_loss : 6.094439506530762 Epoch : 1/10 ... Step : 100 ... d_loss : 1.3318357467651367 ... g_loss : 0.6794193983078003
Epoch : 1/10 ... Step : 110 ... d_loss : 1.397844910621643 ... g_loss : 3.883117198944092 Epoch : 1/10 ... Step : 120 ... d_loss : 1.729637861251831 ... g_loss : 0.9159450531005859 Epoch : 1/10 ... Step : 130 ... d_loss : 0.9159771203994751 ... g_loss : 2.8071227073669434 Epoch : 1/10 ... Step : 140 ... d_loss : 0.7850979566574097 ... g_loss : 2.634237289428711 Epoch : 1/10 ... Step : 150 ... d_loss : 0.7400979995727539 ... g_loss : 3.4742345809936523 Epoch : 1/10 ... Step : 160 ... d_loss : 1.6995909214019775 ... g_loss : 0.6127923727035522 Epoch : 1/10 ... Step : 170 ... d_loss : 1.0672526359558105 ... g_loss : 2.419649600982666 Epoch : 1/10 ... Step : 180 ... d_loss : 2.686013698577881 ... g_loss : 0.673509955406189 Epoch : 1/10 ... Step : 190 ... d_loss : 1.4837532043457031 ... g_loss : 4.456912040710449 Epoch : 1/10 ... Step : 200 ... d_loss : 0.5718165636062622 ... g_loss : 3.2093491554260254
Epoch : 1/10 ... Step : 210 ... d_loss : 1.0083123445510864 ... g_loss : 2.3459014892578125 Epoch : 1/10 ... Step : 220 ... d_loss : 2.623124361038208 ... g_loss : 1.6884279251098633 Epoch : 1/10 ... Step : 230 ... d_loss : 1.198453426361084 ... g_loss : 2.7551279067993164 Epoch : 1/10 ... Step : 240 ... d_loss : 0.45371878147125244 ... g_loss : 3.0702357292175293 Epoch : 1/10 ... Step : 250 ... d_loss : 1.5990288257598877 ... g_loss : 1.3336373567581177 Epoch : 1/10 ... Step : 260 ... d_loss : 0.5275869369506836 ... g_loss : 2.115786552429199 Epoch : 1/10 ... Step : 270 ... d_loss : 1.4636509418487549 ... g_loss : 3.59511661529541 Epoch : 1/10 ... Step : 280 ... d_loss : 0.9516904354095459 ... g_loss : 3.3252086639404297 Epoch : 1/10 ... Step : 290 ... d_loss : 0.7114964723587036 ... g_loss : 3.264331817626953 Epoch : 1/10 ... Step : 300 ... d_loss : 0.8348485231399536 ... g_loss : 2.270519256591797
Epoch : 1/10 ... Step : 310 ... d_loss : 1.294409155845642 ... g_loss : 1.336060881614685 Epoch : 1/10 ... Step : 320 ... d_loss : 0.921809196472168 ... g_loss : 5.333911895751953 Epoch : 1/10 ... Step : 330 ... d_loss : 0.895397424697876 ... g_loss : 2.6435799598693848 Epoch : 1/10 ... Step : 340 ... d_loss : 0.8738511204719543 ... g_loss : 3.386038064956665 Epoch : 1/10 ... Step : 350 ... d_loss : 0.620734691619873 ... g_loss : 3.7486414909362793 Epoch : 1/10 ... Step : 360 ... d_loss : 1.0144203901290894 ... g_loss : 1.469957947731018 Epoch : 1/10 ... Step : 370 ... d_loss : 0.39007750153541565 ... g_loss : 3.1703720092773438 Epoch : 1/10 ... Step : 380 ... d_loss : 1.1083438396453857 ... g_loss : 1.4196758270263672 Epoch : 1/10 ... Step : 390 ... d_loss : 0.8865296840667725 ... g_loss : 1.097640872001648 Epoch : 1/10 ... Step : 400 ... d_loss : 1.1189064979553223 ... g_loss : 1.8318194150924683
Epoch : 1/10 ... Step : 410 ... d_loss : 1.3792486190795898 ... g_loss : 3.445786952972412 Epoch : 1/10 ... Step : 420 ... d_loss : 1.1093313694000244 ... g_loss : 2.5241875648498535 Epoch : 1/10 ... Step : 430 ... d_loss : 0.9715054035186768 ... g_loss : 3.209750175476074 Epoch : 1/10 ... Step : 440 ... d_loss : 1.544184684753418 ... g_loss : 5.252941608428955 Epoch : 1/10 ... Step : 450 ... d_loss : 0.4542277455329895 ... g_loss : 2.635376453399658 Epoch : 1/10 ... Step : 460 ... d_loss : 1.1781299114227295 ... g_loss : 3.10211443901062 Epoch : 1/10 ... Step : 470 ... d_loss : 1.722873330116272 ... g_loss : 0.3512219488620758 Epoch : 1/10 ... Step : 480 ... d_loss : 1.0127923488616943 ... g_loss : 2.341762065887451 Epoch : 1/10 ... Step : 490 ... d_loss : 1.004692554473877 ... g_loss : 2.802274227142334 Epoch : 1/10 ... Step : 500 ... d_loss : 1.08346426486969 ... g_loss : 1.052732229232788
Epoch : 1/10 ... Step : 510 ... d_loss : 0.8115843534469604 ... g_loss : 2.279780864715576 Epoch : 1/10 ... Step : 520 ... d_loss : 1.4663351774215698 ... g_loss : 0.38215139508247375 Epoch : 1/10 ... Step : 530 ... d_loss : 0.8898907899856567 ... g_loss : 2.384796380996704 Epoch : 1/10 ... Step : 540 ... d_loss : 1.2728767395019531 ... g_loss : 1.1691789627075195 Epoch : 1/10 ... Step : 550 ... d_loss : 0.8470571041107178 ... g_loss : 2.0264289379119873 Epoch : 1/10 ... Step : 560 ... d_loss : 1.5382022857666016 ... g_loss : 1.50447678565979 Epoch : 1/10 ... Step : 570 ... d_loss : 1.6164087057113647 ... g_loss : 0.7648814916610718 Epoch : 1/10 ... Step : 580 ... d_loss : 1.1318968534469604 ... g_loss : 1.7330741882324219 Epoch : 1/10 ... Step : 590 ... d_loss : 1.6620967388153076 ... g_loss : 0.4128282070159912 Epoch : 1/10 ... Step : 600 ... d_loss : 1.0595319271087646 ... g_loss : 1.251063346862793
Epoch : 1/10 ... Step : 610 ... d_loss : 1.7486302852630615 ... g_loss : 0.644080400466919 Epoch : 1/10 ... Step : 620 ... d_loss : 1.0599135160446167 ... g_loss : 1.5116643905639648 Epoch : 1/10 ... Step : 630 ... d_loss : 1.2168996334075928 ... g_loss : 0.9227011203765869 Epoch : 1/10 ... Step : 640 ... d_loss : 0.8925243020057678 ... g_loss : 1.7199068069458008 Epoch : 1/10 ... Step : 650 ... d_loss : 1.147553563117981 ... g_loss : 1.5625324249267578 Epoch : 1/10 ... Step : 660 ... d_loss : 0.9455957412719727 ... g_loss : 1.318260669708252 Epoch : 1/10 ... Step : 670 ... d_loss : 1.1988613605499268 ... g_loss : 2.234072685241699 Epoch : 1/10 ... Step : 680 ... d_loss : 0.8029910326004028 ... g_loss : 2.58685302734375 Epoch : 1/10 ... Step : 690 ... d_loss : 0.9784906506538391 ... g_loss : 2.1536436080932617 Epoch : 1/10 ... Step : 700 ... d_loss : 0.847655177116394 ... g_loss : 2.493992328643799
Epoch : 1/10 ... Step : 710 ... d_loss : 1.828971266746521 ... g_loss : 0.4550655782222748 Epoch : 1/10 ... Step : 720 ... d_loss : 1.6097397804260254 ... g_loss : 0.8204618692398071 Epoch : 1/10 ... Step : 730 ... d_loss : 1.0887187719345093 ... g_loss : 2.7606587409973145 Epoch : 1/10 ... Step : 740 ... d_loss : 0.963829517364502 ... g_loss : 1.1489312648773193 Epoch : 1/10 ... Step : 750 ... d_loss : 0.8147303462028503 ... g_loss : 1.6449116468429565 Epoch : 1/10 ... Step : 760 ... d_loss : 1.2644479274749756 ... g_loss : 1.245898962020874 Epoch : 1/10 ... Step : 770 ... d_loss : 2.3815462589263916 ... g_loss : 0.7054839730262756 Epoch : 1/10 ... Step : 780 ... d_loss : 1.0985352993011475 ... g_loss : 1.199989914894104 Epoch : 1/10 ... Step : 790 ... d_loss : 0.8132576942443848 ... g_loss : 2.0180912017822266 Epoch : 1/10 ... Step : 800 ... d_loss : 1.3107726573944092 ... g_loss : 2.2561357021331787
Epoch : 1/10 ... Step : 810 ... d_loss : 1.0638477802276611 ... g_loss : 1.8573417663574219 Epoch : 1/10 ... Step : 820 ... d_loss : 1.1857035160064697 ... g_loss : 0.7945778369903564 Epoch : 1/10 ... Step : 830 ... d_loss : 1.0588810443878174 ... g_loss : 1.7995920181274414 Epoch : 1/10 ... Step : 840 ... d_loss : 0.7991496920585632 ... g_loss : 2.8622632026672363 Epoch : 1/10 ... Step : 850 ... d_loss : 1.6774568557739258 ... g_loss : 0.7666798233985901 Epoch : 1/10 ... Step : 860 ... d_loss : 1.1381988525390625 ... g_loss : 1.804802656173706 Epoch : 1/10 ... Step : 870 ... d_loss : 1.1858789920806885 ... g_loss : 1.1012569665908813 Epoch : 1/10 ... Step : 880 ... d_loss : 1.517936110496521 ... g_loss : 1.2802445888519287 Epoch : 1/10 ... Step : 890 ... d_loss : 0.6709963083267212 ... g_loss : 2.9474101066589355 Epoch : 1/10 ... Step : 900 ... d_loss : 1.611269474029541 ... g_loss : 0.9779760837554932
Epoch : 1/10 ... Step : 910 ... d_loss : 0.7024667263031006 ... g_loss : 2.1487045288085938 Epoch : 1/10 ... Step : 920 ... d_loss : 2.2447454929351807 ... g_loss : 1.576002836227417 Epoch : 1/10 ... Step : 930 ... d_loss : 0.914207398891449 ... g_loss : 4.081455230712891 Epoch : 2/10 ... Step : 940 ... d_loss : 1.1459710597991943 ... g_loss : 1.118256688117981 Epoch : 2/10 ... Step : 950 ... d_loss : 0.8255268335342407 ... g_loss : 2.19569730758667 Epoch : 2/10 ... Step : 960 ... d_loss : 0.8839629292488098 ... g_loss : 2.1204190254211426 Epoch : 2/10 ... Step : 970 ... d_loss : 1.1559247970581055 ... g_loss : 3.868286609649658 Epoch : 2/10 ... Step : 980 ... d_loss : 0.7506850957870483 ... g_loss : 1.4702450037002563 Epoch : 2/10 ... Step : 990 ... d_loss : 1.1529264450073242 ... g_loss : 3.8402624130249023 Epoch : 2/10 ... Step : 1000 ... d_loss : 1.6934853792190552 ... g_loss : 0.965009331703186
Epoch : 2/10 ... Step : 1010 ... d_loss : 1.4895522594451904 ... g_loss : 0.8702187538146973 Epoch : 2/10 ... Step : 1020 ... d_loss : 0.6686224937438965 ... g_loss : 2.087233066558838 Epoch : 2/10 ... Step : 1030 ... d_loss : 1.863518476486206 ... g_loss : 0.7395287156105042 Epoch : 2/10 ... Step : 1040 ... d_loss : 0.7950794696807861 ... g_loss : 2.6093170642852783 Epoch : 2/10 ... Step : 1050 ... d_loss : 0.9540974497795105 ... g_loss : 2.0647072792053223 Epoch : 2/10 ... Step : 1060 ... d_loss : 1.1817119121551514 ... g_loss : 1.0189781188964844 Epoch : 2/10 ... Step : 1070 ... d_loss : 1.6210671663284302 ... g_loss : 1.8388956785202026 Epoch : 2/10 ... Step : 1080 ... d_loss : 0.648138701915741 ... g_loss : 2.9713797569274902 Epoch : 2/10 ... Step : 1090 ... d_loss : 1.0963375568389893 ... g_loss : 1.7738300561904907 Epoch : 2/10 ... Step : 1100 ... d_loss : 0.9361935257911682 ... g_loss : 1.305210828781128
Epoch : 2/10 ... Step : 1110 ... d_loss : 1.0335756540298462 ... g_loss : 1.719269871711731 Epoch : 2/10 ... Step : 1120 ... d_loss : 0.9154480695724487 ... g_loss : 2.6639859676361084 Epoch : 2/10 ... Step : 1130 ... d_loss : 1.3489668369293213 ... g_loss : 2.732447624206543 Epoch : 2/10 ... Step : 1140 ... d_loss : 2.1358413696289062 ... g_loss : 0.8988897800445557 Epoch : 2/10 ... Step : 1150 ... d_loss : 0.8541155457496643 ... g_loss : 1.686530351638794 Epoch : 2/10 ... Step : 1160 ... d_loss : 0.598373532295227 ... g_loss : 2.203688859939575 Epoch : 2/10 ... Step : 1170 ... d_loss : 2.197537660598755 ... g_loss : 0.5535001158714294 Epoch : 2/10 ... Step : 1180 ... d_loss : 0.8037204742431641 ... g_loss : 2.0286293029785156 Epoch : 2/10 ... Step : 1190 ... d_loss : 1.2419915199279785 ... g_loss : 2.5132601261138916 Epoch : 2/10 ... Step : 1200 ... d_loss : 1.2888624668121338 ... g_loss : 0.7693620324134827
Epoch : 2/10 ... Step : 1210 ... d_loss : 0.6594811677932739 ... g_loss : 2.318613052368164 Epoch : 2/10 ... Step : 1220 ... d_loss : 0.7382668256759644 ... g_loss : 1.93937349319458 Epoch : 2/10 ... Step : 1230 ... d_loss : 1.137808084487915 ... g_loss : 2.6914658546447754 Epoch : 2/10 ... Step : 1240 ... d_loss : 0.9283720850944519 ... g_loss : 0.8737508058547974 Epoch : 2/10 ... Step : 1250 ... d_loss : 0.700541615486145 ... g_loss : 2.6104791164398193 Epoch : 2/10 ... Step : 1260 ... d_loss : 0.9245755076408386 ... g_loss : 1.7796504497528076 Epoch : 2/10 ... Step : 1270 ... d_loss : 1.0089993476867676 ... g_loss : 1.4574518203735352 Epoch : 2/10 ... Step : 1280 ... d_loss : 0.767358660697937 ... g_loss : 3.860180377960205 Epoch : 2/10 ... Step : 1290 ... d_loss : 0.687808632850647 ... g_loss : 3.1824092864990234 Epoch : 2/10 ... Step : 1300 ... d_loss : 1.2307770252227783 ... g_loss : 0.9679495096206665
Epoch : 2/10 ... Step : 1310 ... d_loss : 0.8043131828308105 ... g_loss : 2.7005186080932617 Epoch : 2/10 ... Step : 1320 ... d_loss : 1.0527235269546509 ... g_loss : 2.296700954437256 Epoch : 2/10 ... Step : 1330 ... d_loss : 0.9242451190948486 ... g_loss : 2.6920900344848633 Epoch : 2/10 ... Step : 1340 ... d_loss : 1.0833114385604858 ... g_loss : 0.9891119599342346 Epoch : 2/10 ... Step : 1350 ... d_loss : 0.9777916073799133 ... g_loss : 1.3261208534240723 Epoch : 2/10 ... Step : 1360 ... d_loss : 1.1125293970108032 ... g_loss : 2.6040217876434326 Epoch : 2/10 ... Step : 1370 ... d_loss : 1.1208983659744263 ... g_loss : 3.2875571250915527 Epoch : 2/10 ... Step : 1380 ... d_loss : 1.1692968606948853 ... g_loss : 3.6908512115478516 Epoch : 2/10 ... Step : 1390 ... d_loss : 0.659040629863739 ... g_loss : 3.4019718170166016 Epoch : 2/10 ... Step : 1400 ... d_loss : 1.029549479484558 ... g_loss : 3.553986072540283
Epoch : 2/10 ... Step : 1410 ... d_loss : 0.6275672316551208 ... g_loss : 1.2423840761184692 Epoch : 2/10 ... Step : 1420 ... d_loss : 1.2588410377502441 ... g_loss : 2.155533790588379 Epoch : 2/10 ... Step : 1430 ... d_loss : 0.7913228273391724 ... g_loss : 1.539710283279419 Epoch : 2/10 ... Step : 1440 ... d_loss : 1.0762951374053955 ... g_loss : 1.6703287363052368 Epoch : 2/10 ... Step : 1450 ... d_loss : 0.9002818465232849 ... g_loss : 2.6891283988952637 Epoch : 2/10 ... Step : 1460 ... d_loss : 0.9602522850036621 ... g_loss : 3.3632049560546875 Epoch : 2/10 ... Step : 1470 ... d_loss : 1.2924264669418335 ... g_loss : 0.6108856797218323 Epoch : 2/10 ... Step : 1480 ... d_loss : 0.878288209438324 ... g_loss : 2.185183525085449 Epoch : 2/10 ... Step : 1490 ... d_loss : 0.8553974628448486 ... g_loss : 1.1649360656738281 Epoch : 2/10 ... Step : 1500 ... d_loss : 0.3988640010356903 ... g_loss : 2.1921768188476562
Epoch : 2/10 ... Step : 1510 ... d_loss : 0.8318716287612915 ... g_loss : 1.8306682109832764 Epoch : 2/10 ... Step : 1520 ... d_loss : 0.8171424269676208 ... g_loss : 1.9990744590759277 Epoch : 2/10 ... Step : 1530 ... d_loss : 0.7321562767028809 ... g_loss : 2.57387113571167 Epoch : 2/10 ... Step : 1540 ... d_loss : 0.8186473250389099 ... g_loss : 2.6917452812194824 Epoch : 2/10 ... Step : 1550 ... d_loss : 0.6549835205078125 ... g_loss : 3.4486093521118164 Epoch : 2/10 ... Step : 1560 ... d_loss : 0.8387849926948547 ... g_loss : 2.2575533390045166 Epoch : 2/10 ... Step : 1570 ... d_loss : 0.9419991970062256 ... g_loss : 1.3161532878875732 Epoch : 2/10 ... Step : 1580 ... d_loss : 0.6513596773147583 ... g_loss : 2.465937614440918 Epoch : 2/10 ... Step : 1590 ... d_loss : 0.47604960203170776 ... g_loss : 2.618772506713867 Epoch : 2/10 ... Step : 1600 ... d_loss : 0.782699704170227 ... g_loss : 3.274993419647217
Epoch : 2/10 ... Step : 1610 ... d_loss : 1.4192713499069214 ... g_loss : 1.2682976722717285 Epoch : 2/10 ... Step : 1620 ... d_loss : 1.5453224182128906 ... g_loss : 4.8079423904418945 Epoch : 2/10 ... Step : 1630 ... d_loss : 0.7146707773208618 ... g_loss : 2.338101387023926 Epoch : 2/10 ... Step : 1640 ... d_loss : 0.591732382774353 ... g_loss : 1.9977675676345825 Epoch : 2/10 ... Step : 1650 ... d_loss : 0.8881114721298218 ... g_loss : 2.847430944442749 Epoch : 2/10 ... Step : 1660 ... d_loss : 1.460009217262268 ... g_loss : 4.008159637451172 Epoch : 2/10 ... Step : 1670 ... d_loss : 0.2968323826789856 ... g_loss : 2.938028573989868 Epoch : 2/10 ... Step : 1680 ... d_loss : 0.9376636147499084 ... g_loss : 1.5440454483032227 Epoch : 2/10 ... Step : 1690 ... d_loss : 1.5224289894104004 ... g_loss : 1.3404133319854736 Epoch : 2/10 ... Step : 1700 ... d_loss : 0.7500982880592346 ... g_loss : 2.530766487121582
Epoch : 2/10 ... Step : 1710 ... d_loss : 0.8649455308914185 ... g_loss : 2.5496389865875244 Epoch : 2/10 ... Step : 1720 ... d_loss : 0.7861897349357605 ... g_loss : 1.3423779010772705 Epoch : 2/10 ... Step : 1730 ... d_loss : 0.5870214104652405 ... g_loss : 2.47225284576416 Epoch : 2/10 ... Step : 1740 ... d_loss : 0.8374783992767334 ... g_loss : 1.2392834424972534 Epoch : 2/10 ... Step : 1750 ... d_loss : 0.7100834250450134 ... g_loss : 4.410523891448975 Epoch : 2/10 ... Step : 1760 ... d_loss : 0.5476638078689575 ... g_loss : 3.621229887008667 Epoch : 2/10 ... Step : 1770 ... d_loss : 1.1542410850524902 ... g_loss : 3.4449658393859863 Epoch : 2/10 ... Step : 1780 ... d_loss : 1.1973800659179688 ... g_loss : 0.6177740693092346 Epoch : 2/10 ... Step : 1790 ... d_loss : 0.7304009199142456 ... g_loss : 1.9425467252731323 Epoch : 2/10 ... Step : 1800 ... d_loss : 0.6027825474739075 ... g_loss : 2.5887718200683594
Epoch : 2/10 ... Step : 1810 ... d_loss : 0.5702548027038574 ... g_loss : 2.218031167984009 Epoch : 2/10 ... Step : 1820 ... d_loss : 1.037251591682434 ... g_loss : 1.4851902723312378 Epoch : 2/10 ... Step : 1830 ... d_loss : 0.7664676904678345 ... g_loss : 2.5061397552490234 Epoch : 2/10 ... Step : 1840 ... d_loss : 1.831907868385315 ... g_loss : 3.5493087768554688 Epoch : 2/10 ... Step : 1850 ... d_loss : 1.070662260055542 ... g_loss : 1.228297472000122 Epoch : 2/10 ... Step : 1860 ... d_loss : 0.6384458541870117 ... g_loss : 3.139857292175293 Epoch : 2/10 ... Step : 1870 ... d_loss : 0.8266814351081848 ... g_loss : 2.1404781341552734 Epoch : 3/10 ... Step : 1880 ... d_loss : 0.6876800060272217 ... g_loss : 1.6896257400512695 Epoch : 3/10 ... Step : 1890 ... d_loss : 0.6212671995162964 ... g_loss : 2.795193672180176 Epoch : 3/10 ... Step : 1900 ... d_loss : 0.6258349418640137 ... g_loss : 2.9514169692993164
Epoch : 3/10 ... Step : 1910 ... d_loss : 0.5197024345397949 ... g_loss : 3.576925754547119 Epoch : 3/10 ... Step : 1920 ... d_loss : 0.6789804100990295 ... g_loss : 2.4114770889282227 Epoch : 3/10 ... Step : 1930 ... d_loss : 0.7812007665634155 ... g_loss : 2.6419472694396973 Epoch : 3/10 ... Step : 1940 ... d_loss : 0.559668779373169 ... g_loss : 2.9405384063720703 Epoch : 3/10 ... Step : 1950 ... d_loss : 1.3878861665725708 ... g_loss : 2.41098690032959 Epoch : 3/10 ... Step : 1960 ... d_loss : 1.0843268632888794 ... g_loss : 1.6005196571350098 Epoch : 3/10 ... Step : 1970 ... d_loss : 0.9706768989562988 ... g_loss : 1.6747853755950928 Epoch : 3/10 ... Step : 1980 ... d_loss : 0.8149895071983337 ... g_loss : 2.430111885070801 Epoch : 3/10 ... Step : 1990 ... d_loss : 0.6292214393615723 ... g_loss : 2.4433095455169678 Epoch : 3/10 ... Step : 2000 ... d_loss : 0.9085840582847595 ... g_loss : 2.717501640319824
Epoch : 3/10 ... Step : 2010 ... d_loss : 1.7359578609466553 ... g_loss : 0.39884862303733826 Epoch : 3/10 ... Step : 2020 ... d_loss : 0.7601515054702759 ... g_loss : 1.9833523035049438 Epoch : 3/10 ... Step : 2030 ... d_loss : 0.7207311391830444 ... g_loss : 3.14587140083313 Epoch : 3/10 ... Step : 2040 ... d_loss : 0.9974242448806763 ... g_loss : 1.9572521448135376 Epoch : 3/10 ... Step : 2050 ... d_loss : 0.6693199276924133 ... g_loss : 2.7504584789276123 Epoch : 3/10 ... Step : 2060 ... d_loss : 0.774808406829834 ... g_loss : 1.2190520763397217 Epoch : 3/10 ... Step : 2070 ... d_loss : 0.6059982180595398 ... g_loss : 2.321535110473633 Epoch : 3/10 ... Step : 2080 ... d_loss : 1.9889495372772217 ... g_loss : 2.463627815246582 Epoch : 3/10 ... Step : 2090 ... d_loss : 0.9435879588127136 ... g_loss : 1.9023306369781494 Epoch : 3/10 ... Step : 2100 ... d_loss : 1.3695597648620605 ... g_loss : 1.5434205532073975
Epoch : 3/10 ... Step : 2110 ... d_loss : 0.6556255221366882 ... g_loss : 2.2429826259613037 Epoch : 3/10 ... Step : 2120 ... d_loss : 0.9030722975730896 ... g_loss : 1.0781893730163574 Epoch : 3/10 ... Step : 2130 ... d_loss : 0.6952121257781982 ... g_loss : 1.6060776710510254 Epoch : 3/10 ... Step : 2140 ... d_loss : 1.5196149349212646 ... g_loss : 4.305919647216797 Epoch : 3/10 ... Step : 2150 ... d_loss : 0.5989052653312683 ... g_loss : 3.096273899078369 Epoch : 3/10 ... Step : 2160 ... d_loss : 0.8770992755889893 ... g_loss : 3.782188653945923 Epoch : 3/10 ... Step : 2170 ... d_loss : 0.36531487107276917 ... g_loss : 2.2926363945007324 Epoch : 3/10 ... Step : 2180 ... d_loss : 1.0434901714324951 ... g_loss : 1.2778937816619873 Epoch : 3/10 ... Step : 2190 ... d_loss : 0.879559338092804 ... g_loss : 1.0717111825942993 Epoch : 3/10 ... Step : 2200 ... d_loss : 0.6103466749191284 ... g_loss : 2.1747941970825195
Epoch : 3/10 ... Step : 2210 ... d_loss : 2.3046820163726807 ... g_loss : 0.6199878454208374 Epoch : 3/10 ... Step : 2220 ... d_loss : 0.686621367931366 ... g_loss : 3.0048248767852783 Epoch : 3/10 ... Step : 2230 ... d_loss : 0.9679076075553894 ... g_loss : 3.4784746170043945 Epoch : 3/10 ... Step : 2240 ... d_loss : 0.6213066577911377 ... g_loss : 2.5133588314056396 Epoch : 3/10 ... Step : 2250 ... d_loss : 1.1582366228103638 ... g_loss : 3.9821929931640625 Epoch : 3/10 ... Step : 2260 ... d_loss : 0.5526448488235474 ... g_loss : 2.8499743938446045 Epoch : 3/10 ... Step : 2270 ... d_loss : 1.188005805015564 ... g_loss : 2.000993251800537 Epoch : 3/10 ... Step : 2280 ... d_loss : 0.798149585723877 ... g_loss : 1.4764539003372192 Epoch : 3/10 ... Step : 2290 ... d_loss : 1.1022878885269165 ... g_loss : 4.312058448791504 Epoch : 3/10 ... Step : 2300 ... d_loss : 0.6734094619750977 ... g_loss : 3.18026065826416
Epoch : 3/10 ... Step : 2310 ... d_loss : 0.3153733015060425 ... g_loss : 2.9962711334228516 Epoch : 3/10 ... Step : 2320 ... d_loss : 0.6245518326759338 ... g_loss : 1.6377334594726562 Epoch : 3/10 ... Step : 2330 ... d_loss : 0.7013351321220398 ... g_loss : 2.2432076930999756 Epoch : 3/10 ... Step : 2340 ... d_loss : 0.6023874878883362 ... g_loss : 4.073761940002441 Epoch : 3/10 ... Step : 2350 ... d_loss : 0.8917260766029358 ... g_loss : 2.1076507568359375 Epoch : 3/10 ... Step : 2360 ... d_loss : 0.8381975889205933 ... g_loss : 5.4127678871154785 Epoch : 3/10 ... Step : 2370 ... d_loss : 1.00754976272583 ... g_loss : 2.9876561164855957 Epoch : 3/10 ... Step : 2380 ... d_loss : 0.8881943225860596 ... g_loss : 2.7378804683685303 Epoch : 3/10 ... Step : 2390 ... d_loss : 0.5369030237197876 ... g_loss : 3.002049684524536 Epoch : 3/10 ... Step : 2400 ... d_loss : 0.79323410987854 ... g_loss : 1.5068964958190918
Epoch : 3/10 ... Step : 2410 ... d_loss : 0.6769485473632812 ... g_loss : 1.923241376876831 Epoch : 3/10 ... Step : 2420 ... d_loss : 0.8488559722900391 ... g_loss : 3.638395071029663 Epoch : 3/10 ... Step : 2430 ... d_loss : 0.358085960149765 ... g_loss : 2.333353042602539 Epoch : 3/10 ... Step : 2440 ... d_loss : 0.4908442497253418 ... g_loss : 2.1531639099121094 Epoch : 3/10 ... Step : 2450 ... d_loss : 0.9898642897605896 ... g_loss : 3.6340107917785645 Epoch : 3/10 ... Step : 2460 ... d_loss : 0.6031939387321472 ... g_loss : 1.9693613052368164 Epoch : 3/10 ... Step : 2470 ... d_loss : 0.46483170986175537 ... g_loss : 3.302992582321167 Epoch : 3/10 ... Step : 2480 ... d_loss : 0.31832700967788696 ... g_loss : 3.3781938552856445 Epoch : 3/10 ... Step : 2490 ... d_loss : 0.2872273623943329 ... g_loss : 3.1563472747802734 Epoch : 3/10 ... Step : 2500 ... d_loss : 1.1135830879211426 ... g_loss : 4.903600692749023
Epoch : 3/10 ... Step : 2510 ... d_loss : 0.8707605600357056 ... g_loss : 1.0167261362075806 Epoch : 3/10 ... Step : 2520 ... d_loss : 0.40271222591400146 ... g_loss : 2.328646659851074 Epoch : 3/10 ... Step : 2530 ... d_loss : 1.1393910646438599 ... g_loss : 5.431860446929932 Epoch : 3/10 ... Step : 2540 ... d_loss : 0.8106052279472351 ... g_loss : 1.556598424911499 Epoch : 3/10 ... Step : 2550 ... d_loss : 0.3620927333831787 ... g_loss : 3.2217016220092773 Epoch : 3/10 ... Step : 2560 ... d_loss : 1.5505598783493042 ... g_loss : 0.66895592212677 Epoch : 3/10 ... Step : 2570 ... d_loss : 0.556361198425293 ... g_loss : 3.652507781982422 Epoch : 3/10 ... Step : 2580 ... d_loss : 0.29966843128204346 ... g_loss : 3.71964955329895 Epoch : 3/10 ... Step : 2590 ... d_loss : 0.7287103533744812 ... g_loss : 0.6968677043914795 Epoch : 3/10 ... Step : 2600 ... d_loss : 1.117975115776062 ... g_loss : 2.6140875816345215
Epoch : 3/10 ... Step : 2610 ... d_loss : 0.3603280782699585 ... g_loss : 3.121763229370117 Epoch : 3/10 ... Step : 2620 ... d_loss : 1.600315809249878 ... g_loss : 2.963735818862915 Epoch : 3/10 ... Step : 2630 ... d_loss : 0.6509348154067993 ... g_loss : 3.90339994430542 Epoch : 3/10 ... Step : 2640 ... d_loss : 0.7800222039222717 ... g_loss : 1.5005600452423096 Epoch : 3/10 ... Step : 2650 ... d_loss : 0.374279260635376 ... g_loss : 3.020594358444214 Epoch : 3/10 ... Step : 2660 ... d_loss : 0.24396337568759918 ... g_loss : 3.294424533843994 Epoch : 3/10 ... Step : 2670 ... d_loss : 0.3944002687931061 ... g_loss : 3.977750301361084 Epoch : 3/10 ... Step : 2680 ... d_loss : 1.8824516534805298 ... g_loss : 1.6367048025131226 Epoch : 3/10 ... Step : 2690 ... d_loss : 1.1978286504745483 ... g_loss : 1.0190393924713135 Epoch : 3/10 ... Step : 2700 ... d_loss : 1.0392440557479858 ... g_loss : 1.450859785079956
Epoch : 3/10 ... Step : 2710 ... d_loss : 0.5825746059417725 ... g_loss : 2.6642203330993652 Epoch : 3/10 ... Step : 2720 ... d_loss : 0.46029916405677795 ... g_loss : 3.378830909729004 Epoch : 3/10 ... Step : 2730 ... d_loss : 0.9298189878463745 ... g_loss : 4.683013439178467 Epoch : 3/10 ... Step : 2740 ... d_loss : 0.672548770904541 ... g_loss : 2.3659117221832275 Epoch : 3/10 ... Step : 2750 ... d_loss : 1.2758187055587769 ... g_loss : 0.3350875973701477 Epoch : 3/10 ... Step : 2760 ... d_loss : 0.8044378757476807 ... g_loss : 1.8762757778167725 Epoch : 3/10 ... Step : 2770 ... d_loss : 0.8277552723884583 ... g_loss : 2.5451672077178955 Epoch : 3/10 ... Step : 2780 ... d_loss : 0.4586849808692932 ... g_loss : 2.7506027221679688 Epoch : 3/10 ... Step : 2790 ... d_loss : 0.41380882263183594 ... g_loss : 3.410491704940796 Epoch : 3/10 ... Step : 2800 ... d_loss : 0.6617178916931152 ... g_loss : 2.8000431060791016
Epoch : 3/10 ... Step : 2810 ... d_loss : 1.3852107524871826 ... g_loss : 2.2600693702697754 Epoch : 4/10 ... Step : 2820 ... d_loss : 0.45702803134918213 ... g_loss : 2.2762198448181152 Epoch : 4/10 ... Step : 2830 ... d_loss : 0.6500443816184998 ... g_loss : 3.176935911178589 Epoch : 4/10 ... Step : 2840 ... d_loss : 0.5311230421066284 ... g_loss : 2.1560308933258057 Epoch : 4/10 ... Step : 2850 ... d_loss : 1.0627245903015137 ... g_loss : 1.6787950992584229 Epoch : 4/10 ... Step : 2860 ... d_loss : 0.7563425898551941 ... g_loss : 5.428862571716309 Epoch : 4/10 ... Step : 2870 ... d_loss : 0.388843834400177 ... g_loss : 3.382420778274536 Epoch : 4/10 ... Step : 2880 ... d_loss : 0.12524618208408356 ... g_loss : 4.579204082489014 Epoch : 4/10 ... Step : 2890 ... d_loss : 2.167823314666748 ... g_loss : 5.2863616943359375 Epoch : 4/10 ... Step : 2900 ... d_loss : 1.2969845533370972 ... g_loss : 0.8685736656188965
Epoch : 4/10 ... Step : 2910 ... d_loss : 0.687046468257904 ... g_loss : 1.8076841831207275 Epoch : 4/10 ... Step : 2920 ... d_loss : 0.6872557401657104 ... g_loss : 1.9326372146606445 Epoch : 4/10 ... Step : 2930 ... d_loss : 0.5128529071807861 ... g_loss : 5.673361778259277 Epoch : 4/10 ... Step : 2940 ... d_loss : 0.6029564142227173 ... g_loss : 2.9857351779937744 Epoch : 4/10 ... Step : 2950 ... d_loss : 0.9304505586624146 ... g_loss : 2.9312610626220703 Epoch : 4/10 ... Step : 2960 ... d_loss : 1.1422818899154663 ... g_loss : 3.015167236328125 Epoch : 4/10 ... Step : 2970 ... d_loss : 0.33338463306427 ... g_loss : 2.993155002593994 Epoch : 4/10 ... Step : 2980 ... d_loss : 0.667069673538208 ... g_loss : 2.1975338459014893 Epoch : 4/10 ... Step : 2990 ... d_loss : 0.395591676235199 ... g_loss : 3.7292847633361816 Epoch : 4/10 ... Step : 3000 ... d_loss : 0.29806387424468994 ... g_loss : 2.7630906105041504
Epoch : 4/10 ... Step : 3010 ... d_loss : 0.3685765862464905 ... g_loss : 2.047623634338379 Epoch : 4/10 ... Step : 3020 ... d_loss : 0.5661174654960632 ... g_loss : 4.464141845703125 Epoch : 4/10 ... Step : 3030 ... d_loss : 0.7662981152534485 ... g_loss : 2.4819178581237793 Epoch : 4/10 ... Step : 3040 ... d_loss : 0.3648286759853363 ... g_loss : 2.7499399185180664 Epoch : 4/10 ... Step : 3050 ... d_loss : 0.18059372901916504 ... g_loss : 4.026214122772217 Epoch : 4/10 ... Step : 3060 ... d_loss : 0.323852002620697 ... g_loss : 2.0818588733673096 Epoch : 4/10 ... Step : 3070 ... d_loss : 0.2890133261680603 ... g_loss : 3.380502223968506 Epoch : 4/10 ... Step : 3080 ... d_loss : 0.6702040433883667 ... g_loss : 1.7713932991027832 Epoch : 4/10 ... Step : 3090 ... d_loss : 1.7228559255599976 ... g_loss : 1.4509375095367432 Epoch : 4/10 ... Step : 3100 ... d_loss : 0.7480499744415283 ... g_loss : 3.919896125793457
Epoch : 4/10 ... Step : 3110 ... d_loss : 0.303931325674057 ... g_loss : 3.6131787300109863 Epoch : 4/10 ... Step : 3120 ... d_loss : 0.1991620808839798 ... g_loss : 3.510369062423706 Epoch : 4/10 ... Step : 3130 ... d_loss : 0.22725118696689606 ... g_loss : 3.131316661834717 Epoch : 4/10 ... Step : 3140 ... d_loss : 0.2848154902458191 ... g_loss : 2.7988219261169434 Epoch : 4/10 ... Step : 3150 ... d_loss : 0.1549346148967743 ... g_loss : 4.549923896789551 Epoch : 4/10 ... Step : 3160 ... d_loss : 0.3157719075679779 ... g_loss : 3.732738494873047 Epoch : 4/10 ... Step : 3170 ... d_loss : 0.2435213029384613 ... g_loss : 4.425310134887695 Epoch : 4/10 ... Step : 3180 ... d_loss : 0.18149979412555695 ... g_loss : 3.263108730316162 Epoch : 4/10 ... Step : 3190 ... d_loss : 0.3247827887535095 ... g_loss : 3.3761396408081055 Epoch : 4/10 ... Step : 3200 ... d_loss : 0.15854185819625854 ... g_loss : 3.8315858840942383
Epoch : 4/10 ... Step : 3210 ... d_loss : 0.18810072541236877 ... g_loss : 3.645890235900879 Epoch : 4/10 ... Step : 3220 ... d_loss : 0.3073476254940033 ... g_loss : 4.7177228927612305 Epoch : 4/10 ... Step : 3230 ... d_loss : 0.9789868593215942 ... g_loss : 4.540009498596191 Epoch : 4/10 ... Step : 3240 ... d_loss : 0.7767301201820374 ... g_loss : 3.1381471157073975 Epoch : 4/10 ... Step : 3250 ... d_loss : 2.361053705215454 ... g_loss : 2.22110652923584 Epoch : 4/10 ... Step : 3260 ... d_loss : 0.7166042327880859 ... g_loss : 3.7539820671081543 Epoch : 4/10 ... Step : 3270 ... d_loss : 1.375674843788147 ... g_loss : 5.640787124633789 Epoch : 4/10 ... Step : 3280 ... d_loss : 0.36544978618621826 ... g_loss : 3.003166675567627 Epoch : 4/10 ... Step : 3290 ... d_loss : 0.3757520020008087 ... g_loss : 3.5299839973449707 Epoch : 4/10 ... Step : 3300 ... d_loss : 0.4508762061595917 ... g_loss : 2.182831048965454
Epoch : 4/10 ... Step : 3310 ... d_loss : 0.6498488783836365 ... g_loss : 3.083042621612549 Epoch : 4/10 ... Step : 3320 ... d_loss : 0.5324718952178955 ... g_loss : 1.6175322532653809 Epoch : 4/10 ... Step : 3330 ... d_loss : 0.5264775156974792 ... g_loss : 3.6978354454040527 Epoch : 4/10 ... Step : 3340 ... d_loss : 0.6177459359169006 ... g_loss : 3.8493094444274902 Epoch : 4/10 ... Step : 3350 ... d_loss : 0.20867586135864258 ... g_loss : 4.148177623748779 Epoch : 4/10 ... Step : 3360 ... d_loss : 0.2767804265022278 ... g_loss : 4.776662826538086 Epoch : 4/10 ... Step : 3370 ... d_loss : 0.5793380737304688 ... g_loss : 1.5093721151351929 Epoch : 4/10 ... Step : 3380 ... d_loss : 0.5587499737739563 ... g_loss : 3.6375603675842285 Epoch : 4/10 ... Step : 3390 ... d_loss : 0.48746174573898315 ... g_loss : 3.7844293117523193 Epoch : 4/10 ... Step : 3400 ... d_loss : 0.11110837012529373 ... g_loss : 4.008070945739746
Epoch : 4/10 ... Step : 3410 ... d_loss : 0.11344379186630249 ... g_loss : 3.6011390686035156 Epoch : 4/10 ... Step : 3420 ... d_loss : 0.15925711393356323 ... g_loss : 3.7038583755493164 Epoch : 4/10 ... Step : 3430 ... d_loss : 0.4873153567314148 ... g_loss : 2.8229355812072754 Epoch : 4/10 ... Step : 3440 ... d_loss : 0.8319832682609558 ... g_loss : 1.2833715677261353 Epoch : 4/10 ... Step : 3450 ... d_loss : 0.9539816379547119 ... g_loss : 0.9888925552368164 Epoch : 4/10 ... Step : 3460 ... d_loss : 0.7331333756446838 ... g_loss : 1.6842947006225586 Epoch : 4/10 ... Step : 3470 ... d_loss : 0.4065011739730835 ... g_loss : 3.690208911895752 Epoch : 4/10 ... Step : 3480 ... d_loss : 0.4153558909893036 ... g_loss : 4.496819019317627 Epoch : 4/10 ... Step : 3490 ... d_loss : 1.5476081371307373 ... g_loss : 9.470739364624023 Epoch : 4/10 ... Step : 3500 ... d_loss : 0.6158453226089478 ... g_loss : 3.0617685317993164
Epoch : 4/10 ... Step : 3510 ... d_loss : 0.7848144769668579 ... g_loss : 2.565505027770996 Epoch : 4/10 ... Step : 3520 ... d_loss : 0.3313739597797394 ... g_loss : 3.5445446968078613 Epoch : 4/10 ... Step : 3530 ... d_loss : 0.6133471131324768 ... g_loss : 2.4567627906799316 Epoch : 4/10 ... Step : 3540 ... d_loss : 0.603750467300415 ... g_loss : 2.6196541786193848 Epoch : 4/10 ... Step : 3550 ... d_loss : 0.6095855832099915 ... g_loss : 2.249553918838501 Epoch : 4/10 ... Step : 3560 ... d_loss : 0.297860324382782 ... g_loss : 4.130716323852539 Epoch : 4/10 ... Step : 3570 ... d_loss : 0.2864093780517578 ... g_loss : 2.9085140228271484 Epoch : 4/10 ... Step : 3580 ... d_loss : 1.126969337463379 ... g_loss : 5.724924087524414 Epoch : 4/10 ... Step : 3590 ... d_loss : 0.2864258289337158 ... g_loss : 2.5035674571990967 Epoch : 4/10 ... Step : 3600 ... d_loss : 0.6529532670974731 ... g_loss : 2.859992027282715
Epoch : 4/10 ... Step : 3610 ... d_loss : 0.9330975413322449 ... g_loss : 1.4175052642822266 Epoch : 4/10 ... Step : 3620 ... d_loss : 0.25010350346565247 ... g_loss : 3.7199511528015137 Epoch : 4/10 ... Step : 3630 ... d_loss : 0.0972704365849495 ... g_loss : 4.251500606536865 Epoch : 4/10 ... Step : 3640 ... d_loss : 0.14907586574554443 ... g_loss : 4.038156509399414 Epoch : 4/10 ... Step : 3650 ... d_loss : 0.24602647125720978 ... g_loss : 3.654001474380493 Epoch : 4/10 ... Step : 3660 ... d_loss : 0.22192548215389252 ... g_loss : 4.000104904174805 Epoch : 4/10 ... Step : 3670 ... d_loss : 0.3003673553466797 ... g_loss : 4.187995910644531 Epoch : 4/10 ... Step : 3680 ... d_loss : 0.30468297004699707 ... g_loss : 3.5094783306121826 Epoch : 4/10 ... Step : 3690 ... d_loss : 0.17073962092399597 ... g_loss : 4.923833847045898 Epoch : 4/10 ... Step : 3700 ... d_loss : 0.12310756742954254 ... g_loss : 4.2938737869262695
Epoch : 4/10 ... Step : 3710 ... d_loss : 0.08356243371963501 ... g_loss : 5.685659408569336 Epoch : 4/10 ... Step : 3720 ... d_loss : 0.06300653517246246 ... g_loss : 4.632534980773926 Epoch : 4/10 ... Step : 3730 ... d_loss : 0.1295255422592163 ... g_loss : 4.906928062438965 Epoch : 4/10 ... Step : 3740 ... d_loss : 2.575941324234009 ... g_loss : 5.13797664642334 Epoch : 5/10 ... Step : 3750 ... d_loss : 1.1848102807998657 ... g_loss : 4.170968055725098 Epoch : 5/10 ... Step : 3760 ... d_loss : 0.5407878160476685 ... g_loss : 1.52827787399292 Epoch : 5/10 ... Step : 3770 ... d_loss : 0.8294597268104553 ... g_loss : 3.2291295528411865 Epoch : 5/10 ... Step : 3780 ... d_loss : 1.4497365951538086 ... g_loss : 1.2825380563735962 Epoch : 5/10 ... Step : 3790 ... d_loss : 0.8172213435173035 ... g_loss : 3.130376100540161 Epoch : 5/10 ... Step : 3800 ... d_loss : 0.28973388671875 ... g_loss : 3.098207950592041
Epoch : 5/10 ... Step : 3810 ... d_loss : 0.30199676752090454 ... g_loss : 3.961540699005127 Epoch : 5/10 ... Step : 3820 ... d_loss : 0.4007960855960846 ... g_loss : 4.720726013183594 Epoch : 5/10 ... Step : 3830 ... d_loss : 0.2338714897632599 ... g_loss : 4.461013317108154 Epoch : 5/10 ... Step : 3840 ... d_loss : 0.1842723786830902 ... g_loss : 3.769836902618408 Epoch : 5/10 ... Step : 3850 ... d_loss : 0.2158692330121994 ... g_loss : 3.9970006942749023 Epoch : 5/10 ... Step : 3860 ... d_loss : 0.2237279713153839 ... g_loss : 2.797482967376709 Epoch : 5/10 ... Step : 3870 ... d_loss : 0.5657139420509338 ... g_loss : 2.9233148097991943 Epoch : 5/10 ... Step : 3880 ... d_loss : 0.2623926103115082 ... g_loss : 3.046438694000244 Epoch : 5/10 ... Step : 3890 ... d_loss : 0.7554762363433838 ... g_loss : 1.768021583557129 Epoch : 5/10 ... Step : 3900 ... d_loss : 0.7378473877906799 ... g_loss : 2.2340898513793945
Epoch : 5/10 ... Step : 3910 ... d_loss : 0.6278896331787109 ... g_loss : 3.8769845962524414 Epoch : 5/10 ... Step : 3920 ... d_loss : 0.7005281448364258 ... g_loss : 1.427491307258606 Epoch : 5/10 ... Step : 3930 ... d_loss : 0.45041126012802124 ... g_loss : 2.359966278076172 Epoch : 5/10 ... Step : 3940 ... d_loss : 1.2242149114608765 ... g_loss : 1.6982060670852661 Epoch : 5/10 ... Step : 3950 ... d_loss : 0.45797258615493774 ... g_loss : 2.7823126316070557 Epoch : 5/10 ... Step : 3960 ... d_loss : 0.649852454662323 ... g_loss : 2.1703662872314453 Epoch : 5/10 ... Step : 3970 ... d_loss : 0.22654178738594055 ... g_loss : 3.5228469371795654 Epoch : 5/10 ... Step : 3980 ... d_loss : 0.11625228822231293 ... g_loss : 3.5240306854248047 Epoch : 5/10 ... Step : 3990 ... d_loss : 0.14857754111289978 ... g_loss : 4.549731254577637 Epoch : 5/10 ... Step : 4000 ... d_loss : 0.4691773056983948 ... g_loss : 4.446135520935059
Epoch : 5/10 ... Step : 4010 ... d_loss : 0.4751555323600769 ... g_loss : 3.19518780708313 Epoch : 5/10 ... Step : 4020 ... d_loss : 0.12418337911367416 ... g_loss : 4.4914045333862305 Epoch : 5/10 ... Step : 4030 ... d_loss : 0.23061558604240417 ... g_loss : 3.3177013397216797 Epoch : 5/10 ... Step : 4040 ... d_loss : 0.27767473459243774 ... g_loss : 4.846829891204834 Epoch : 5/10 ... Step : 4050 ... d_loss : 0.05975072458386421 ... g_loss : 4.194761753082275 Epoch : 5/10 ... Step : 4060 ... d_loss : 0.11241286993026733 ... g_loss : 4.611127853393555 Epoch : 5/10 ... Step : 4070 ... d_loss : 0.09381356835365295 ... g_loss : 3.784818172454834 Epoch : 5/10 ... Step : 4080 ... d_loss : 0.04444434493780136 ... g_loss : 4.998353481292725 Epoch : 5/10 ... Step : 4090 ... d_loss : 0.09211961925029755 ... g_loss : 5.126161575317383 Epoch : 5/10 ... Step : 4100 ... d_loss : 0.26266777515411377 ... g_loss : 4.409296035766602
Epoch : 5/10 ... Step : 4110 ... d_loss : 0.06199003756046295 ... g_loss : 4.327274322509766 Epoch : 5/10 ... Step : 4120 ... d_loss : 0.18574482202529907 ... g_loss : 4.032162666320801 Epoch : 5/10 ... Step : 4130 ... d_loss : 2.7659823894500732 ... g_loss : 1.8919880390167236 Epoch : 5/10 ... Step : 4140 ... d_loss : 0.45955657958984375 ... g_loss : 3.283599376678467 Epoch : 5/10 ... Step : 4150 ... d_loss : 0.21579252183437347 ... g_loss : 5.211306571960449 Epoch : 5/10 ... Step : 4160 ... d_loss : 0.17635683715343475 ... g_loss : 4.601237773895264 Epoch : 5/10 ... Step : 4170 ... d_loss : 0.3069441020488739 ... g_loss : 4.269556045532227 Epoch : 5/10 ... Step : 4180 ... d_loss : 1.7468127012252808 ... g_loss : 2.2572314739227295 Epoch : 5/10 ... Step : 4190 ... d_loss : 0.8008681535720825 ... g_loss : 1.268168330192566 Epoch : 5/10 ... Step : 4200 ... d_loss : 0.638542115688324 ... g_loss : 3.481302261352539
Epoch : 5/10 ... Step : 4210 ... d_loss : 0.6679546236991882 ... g_loss : 6.311999797821045 Epoch : 5/10 ... Step : 4220 ... d_loss : 0.35762178897857666 ... g_loss : 2.7695038318634033 Epoch : 5/10 ... Step : 4230 ... d_loss : 0.214121013879776 ... g_loss : 4.080080032348633 Epoch : 5/10 ... Step : 4240 ... d_loss : 0.18001022934913635 ... g_loss : 3.85597562789917 Epoch : 5/10 ... Step : 4250 ... d_loss : 0.19117939472198486 ... g_loss : 3.643832206726074 Epoch : 5/10 ... Step : 4260 ... d_loss : 0.9693663120269775 ... g_loss : 1.2476682662963867 Epoch : 5/10 ... Step : 4270 ... d_loss : 0.7178347110748291 ... g_loss : 4.106188774108887 Epoch : 5/10 ... Step : 4280 ... d_loss : 0.7761889100074768 ... g_loss : 6.079099655151367 Epoch : 5/10 ... Step : 4290 ... d_loss : 1.452149510383606 ... g_loss : 1.367631196975708 Epoch : 5/10 ... Step : 4300 ... d_loss : 0.17729564011096954 ... g_loss : 3.957515239715576
Epoch : 5/10 ... Step : 4310 ... d_loss : 0.2014431357383728 ... g_loss : 3.691737413406372 Epoch : 5/10 ... Step : 4320 ... d_loss : 0.37892991304397583 ... g_loss : 2.5031509399414062 Epoch : 5/10 ... Step : 4330 ... d_loss : 1.003964900970459 ... g_loss : 5.2112274169921875 Epoch : 5/10 ... Step : 4340 ... d_loss : 0.526769757270813 ... g_loss : 3.593379020690918 Epoch : 5/10 ... Step : 4350 ... d_loss : 1.0095105171203613 ... g_loss : 1.7271792888641357 Epoch : 5/10 ... Step : 4360 ... d_loss : 0.7087276577949524 ... g_loss : 2.101011276245117 Epoch : 5/10 ... Step : 4370 ... d_loss : 0.8372423052787781 ... g_loss : 5.273873329162598 Epoch : 5/10 ... Step : 4380 ... d_loss : 0.29693466424942017 ... g_loss : 3.5812125205993652 Epoch : 5/10 ... Step : 4390 ... d_loss : 0.280076801776886 ... g_loss : 3.9312872886657715 Epoch : 5/10 ... Step : 4400 ... d_loss : 0.22727635502815247 ... g_loss : 3.3000872135162354
Epoch : 5/10 ... Step : 4410 ... d_loss : 0.1030583381652832 ... g_loss : 4.3264875411987305 Epoch : 5/10 ... Step : 4420 ... d_loss : 0.9289167523384094 ... g_loss : 1.6585991382598877 Epoch : 5/10 ... Step : 4430 ... d_loss : 0.664890468120575 ... g_loss : 3.1719794273376465 Epoch : 5/10 ... Step : 4440 ... d_loss : 0.4225424528121948 ... g_loss : 2.2871809005737305 Epoch : 5/10 ... Step : 4450 ... d_loss : 0.28235024213790894 ... g_loss : 3.2911791801452637 Epoch : 5/10 ... Step : 4460 ... d_loss : 0.37095654010772705 ... g_loss : 5.868144989013672 Epoch : 5/10 ... Step : 4470 ... d_loss : 1.3176757097244263 ... g_loss : 1.2114534378051758 Epoch : 5/10 ... Step : 4480 ... d_loss : 0.21857789158821106 ... g_loss : 4.154777526855469 Epoch : 5/10 ... Step : 4490 ... d_loss : 0.37084436416625977 ... g_loss : 5.204329967498779 Epoch : 5/10 ... Step : 4500 ... d_loss : 1.3902674913406372 ... g_loss : 2.6256492137908936
Epoch : 5/10 ... Step : 4510 ... d_loss : 0.5314638018608093 ... g_loss : 4.368988990783691 Epoch : 5/10 ... Step : 4520 ... d_loss : 0.6655048131942749 ... g_loss : 4.488977432250977 Epoch : 5/10 ... Step : 4530 ... d_loss : 0.602957546710968 ... g_loss : 5.092824459075928 Epoch : 5/10 ... Step : 4540 ... d_loss : 0.27585846185684204 ... g_loss : 3.3631606101989746 Epoch : 5/10 ... Step : 4550 ... d_loss : 0.16824975609779358 ... g_loss : 5.095488548278809 Epoch : 5/10 ... Step : 4560 ... d_loss : 0.5877707004547119 ... g_loss : 4.9968414306640625 Epoch : 5/10 ... Step : 4570 ... d_loss : 0.22984866797924042 ... g_loss : 3.9453210830688477 Epoch : 5/10 ... Step : 4580 ... d_loss : 0.05677759274840355 ... g_loss : 4.944132328033447 Epoch : 5/10 ... Step : 4590 ... d_loss : 0.03351409733295441 ... g_loss : 5.51926326751709 Epoch : 5/10 ... Step : 4600 ... d_loss : 0.08074355125427246 ... g_loss : 4.204440116882324
Epoch : 5/10 ... Step : 4610 ... d_loss : 0.16838602721691132 ... g_loss : 2.9618284702301025 Epoch : 5/10 ... Step : 4620 ... d_loss : 0.23934149742126465 ... g_loss : 4.551494598388672 Epoch : 5/10 ... Step : 4630 ... d_loss : 0.09697059541940689 ... g_loss : 4.092768669128418 Epoch : 5/10 ... Step : 4640 ... d_loss : 0.1027136892080307 ... g_loss : 4.165600776672363 Epoch : 5/10 ... Step : 4650 ... d_loss : 0.32027894258499146 ... g_loss : 2.89556884765625 Epoch : 5/10 ... Step : 4660 ... d_loss : 0.05034470558166504 ... g_loss : 4.694199085235596 Epoch : 5/10 ... Step : 4670 ... d_loss : 0.15547659993171692 ... g_loss : 7.11574649810791 Epoch : 5/10 ... Step : 4680 ... d_loss : 0.05762944743037224 ... g_loss : 6.489585876464844 Epoch : 6/10 ... Step : 4690 ... d_loss : 0.08901570737361908 ... g_loss : 4.865277290344238 Epoch : 6/10 ... Step : 4700 ... d_loss : 0.017642216756939888 ... g_loss : 5.426663398742676
Epoch : 6/10 ... Step : 4710 ... d_loss : 0.09244583547115326 ... g_loss : 4.930052757263184 Epoch : 6/10 ... Step : 4720 ... d_loss : 0.17378990352153778 ... g_loss : 4.629166603088379 Epoch : 6/10 ... Step : 4730 ... d_loss : 0.25510576367378235 ... g_loss : 2.3093581199645996 Epoch : 6/10 ... Step : 4740 ... d_loss : 3.727717161178589 ... g_loss : 2.186795711517334 Epoch : 6/10 ... Step : 4750 ... d_loss : 0.9744437336921692 ... g_loss : 0.9372454285621643 Epoch : 6/10 ... Step : 4760 ... d_loss : 1.0248123407363892 ... g_loss : 2.890535593032837 Epoch : 6/10 ... Step : 4770 ... d_loss : 0.3226929008960724 ... g_loss : 3.0429294109344482 Epoch : 6/10 ... Step : 4780 ... d_loss : 0.3582284152507782 ... g_loss : 4.983440399169922 Epoch : 6/10 ... Step : 4790 ... d_loss : 0.44860678911209106 ... g_loss : 2.138859272003174 Epoch : 6/10 ... Step : 4800 ... d_loss : 0.2846756875514984 ... g_loss : 3.9829049110412598
Epoch : 6/10 ... Step : 4810 ... d_loss : 0.4325634837150574 ... g_loss : 6.341652870178223 Epoch : 6/10 ... Step : 4820 ... d_loss : 1.3134418725967407 ... g_loss : 0.28900381922721863 Epoch : 6/10 ... Step : 4830 ... d_loss : 0.8104652166366577 ... g_loss : 1.4464924335479736 Epoch : 6/10 ... Step : 4840 ... d_loss : 0.17222389578819275 ... g_loss : 4.1913838386535645 Epoch : 6/10 ... Step : 4850 ... d_loss : 0.6410678625106812 ... g_loss : 6.738012313842773 Epoch : 6/10 ... Step : 4860 ... d_loss : 0.6637396812438965 ... g_loss : 4.536620140075684 Epoch : 6/10 ... Step : 4870 ... d_loss : 0.6036925315856934 ... g_loss : 4.238923072814941 Epoch : 6/10 ... Step : 4880 ... d_loss : 0.5395240783691406 ... g_loss : 3.2975401878356934 Epoch : 6/10 ... Step : 4890 ... d_loss : 1.8413379192352295 ... g_loss : 6.752371788024902 Epoch : 6/10 ... Step : 4900 ... d_loss : 1.7458668947219849 ... g_loss : 1.7581886053085327
Epoch : 6/10 ... Step : 4910 ... d_loss : 0.584260106086731 ... g_loss : 3.408860683441162 Epoch : 6/10 ... Step : 4920 ... d_loss : 1.7100110054016113 ... g_loss : 2.6325249671936035 Epoch : 6/10 ... Step : 4930 ... d_loss : 2.0684187412261963 ... g_loss : 1.6965770721435547 Epoch : 6/10 ... Step : 4940 ... d_loss : 1.6089357137680054 ... g_loss : 0.7972310781478882 Epoch : 6/10 ... Step : 4950 ... d_loss : 0.8245968818664551 ... g_loss : 2.0523266792297363 Epoch : 6/10 ... Step : 4960 ... d_loss : 0.7879807353019714 ... g_loss : 1.505892038345337 Epoch : 6/10 ... Step : 4970 ... d_loss : 0.43703868985176086 ... g_loss : 2.9082236289978027 Epoch : 6/10 ... Step : 4980 ... d_loss : 0.1302841305732727 ... g_loss : 3.507145881652832 Epoch : 6/10 ... Step : 4990 ... d_loss : 0.2369925081729889 ... g_loss : 3.9890427589416504 Epoch : 6/10 ... Step : 5000 ... d_loss : 0.1754389852285385 ... g_loss : 4.762545585632324
Epoch : 6/10 ... Step : 5010 ... d_loss : 0.10731343924999237 ... g_loss : 4.340545654296875 Epoch : 6/10 ... Step : 5020 ... d_loss : 0.15229927003383636 ... g_loss : 4.625977993011475 Epoch : 6/10 ... Step : 5030 ... d_loss : 0.18634310364723206 ... g_loss : 3.9593632221221924 Epoch : 6/10 ... Step : 5040 ... d_loss : 0.40611040592193604 ... g_loss : 2.3869094848632812 Epoch : 6/10 ... Step : 5050 ... d_loss : 2.372617244720459 ... g_loss : 1.5564740896224976 Epoch : 6/10 ... Step : 5060 ... d_loss : 0.6907771825790405 ... g_loss : 1.526402235031128 Epoch : 6/10 ... Step : 5070 ... d_loss : 0.8462308049201965 ... g_loss : 2.294734477996826 Epoch : 6/10 ... Step : 5080 ... d_loss : 0.7654008865356445 ... g_loss : 5.4105353355407715 Epoch : 6/10 ... Step : 5090 ... d_loss : 0.4526084065437317 ... g_loss : 3.2282254695892334 Epoch : 6/10 ... Step : 5100 ... d_loss : 0.4154263436794281 ... g_loss : 3.858574867248535
Epoch : 6/10 ... Step : 5110 ... d_loss : 0.521973192691803 ... g_loss : 3.846388816833496 Epoch : 6/10 ... Step : 5120 ... d_loss : 0.08060701936483383 ... g_loss : 4.182920455932617 Epoch : 6/10 ... Step : 5130 ... d_loss : 0.12906888127326965 ... g_loss : 3.8731417655944824 Epoch : 6/10 ... Step : 5140 ... d_loss : 0.10413096100091934 ... g_loss : 4.364293098449707 Epoch : 6/10 ... Step : 5150 ... d_loss : 0.1280343234539032 ... g_loss : 3.780402183532715 Epoch : 6/10 ... Step : 5160 ... d_loss : 0.17279452085494995 ... g_loss : 4.391156196594238 Epoch : 6/10 ... Step : 5170 ... d_loss : 0.06596966087818146 ... g_loss : 4.597660064697266 Epoch : 6/10 ... Step : 5180 ... d_loss : 0.057258106768131256 ... g_loss : 5.050132751464844 Epoch : 6/10 ... Step : 5190 ... d_loss : 0.05264609307050705 ... g_loss : 5.260942459106445 Epoch : 6/10 ... Step : 5200 ... d_loss : 0.5361393690109253 ... g_loss : 2.299546718597412
Epoch : 6/10 ... Step : 5210 ... d_loss : 2.5887258052825928 ... g_loss : 9.098322868347168 Epoch : 6/10 ... Step : 5220 ... d_loss : 0.5946263074874878 ... g_loss : 2.6472322940826416 Epoch : 6/10 ... Step : 5230 ... d_loss : 0.3084946274757385 ... g_loss : 3.7090678215026855 Epoch : 6/10 ... Step : 5240 ... d_loss : 0.15869218111038208 ... g_loss : 5.370797157287598 Epoch : 6/10 ... Step : 5250 ... d_loss : 0.2608226239681244 ... g_loss : 3.4104130268096924 Epoch : 6/10 ... Step : 5260 ... d_loss : 1.0661693811416626 ... g_loss : 1.7110341787338257 Epoch : 6/10 ... Step : 5270 ... d_loss : 0.4426395893096924 ... g_loss : 2.9633960723876953 Epoch : 6/10 ... Step : 5280 ... d_loss : 0.6735856533050537 ... g_loss : 4.177457809448242 Epoch : 6/10 ... Step : 5290 ... d_loss : 0.7438953518867493 ... g_loss : 3.5933148860931396 Epoch : 6/10 ... Step : 5300 ... d_loss : 0.8269935846328735 ... g_loss : 6.014006614685059
Epoch : 6/10 ... Step : 5310 ... d_loss : 0.9638194441795349 ... g_loss : 0.732622504234314 Epoch : 6/10 ... Step : 5320 ... d_loss : 0.3410460352897644 ... g_loss : 4.203519821166992 Epoch : 6/10 ... Step : 5330 ... d_loss : 0.15652620792388916 ... g_loss : 4.495200157165527 Epoch : 6/10 ... Step : 5340 ... d_loss : 0.2633787989616394 ... g_loss : 3.3853278160095215 Epoch : 6/10 ... Step : 5350 ... d_loss : 0.17988166213035583 ... g_loss : 4.339285373687744 Epoch : 6/10 ... Step : 5360 ... d_loss : 0.10880763083696365 ... g_loss : 4.973764419555664 Epoch : 6/10 ... Step : 5370 ... d_loss : 0.030289042741060257 ... g_loss : 5.370233535766602 Epoch : 6/10 ... Step : 5380 ... d_loss : 0.18979348242282867 ... g_loss : 4.259322166442871 Epoch : 6/10 ... Step : 5390 ... d_loss : 0.24773527681827545 ... g_loss : 5.577341079711914 Epoch : 6/10 ... Step : 5400 ... d_loss : 0.012787103652954102 ... g_loss : 5.757557392120361
Epoch : 6/10 ... Step : 5410 ... d_loss : 0.2952938377857208 ... g_loss : 3.1642556190490723 Epoch : 6/10 ... Step : 5420 ... d_loss : 0.4918583333492279 ... g_loss : 3.3072428703308105 Epoch : 6/10 ... Step : 5430 ... d_loss : 0.249958798289299 ... g_loss : 5.0058064460754395 Epoch : 6/10 ... Step : 5440 ... d_loss : 0.20760196447372437 ... g_loss : 4.288188457489014 Epoch : 6/10 ... Step : 5450 ... d_loss : 0.3129323720932007 ... g_loss : 6.400469779968262 Epoch : 6/10 ... Step : 5460 ... d_loss : 0.25979846715927124 ... g_loss : 4.007099151611328 Epoch : 6/10 ... Step : 5470 ... d_loss : 0.5497262477874756 ... g_loss : 5.807229042053223 Epoch : 6/10 ... Step : 5480 ... d_loss : 1.2012939453125 ... g_loss : 6.863430023193359 Epoch : 6/10 ... Step : 5490 ... d_loss : 0.6274760961532593 ... g_loss : 4.416633129119873 Epoch : 6/10 ... Step : 5500 ... d_loss : 1.0762484073638916 ... g_loss : 4.881482124328613
Epoch : 6/10 ... Step : 5510 ... d_loss : 1.0300933122634888 ... g_loss : 4.813640594482422 Epoch : 6/10 ... Step : 5520 ... d_loss : 0.2880244255065918 ... g_loss : 4.281001567840576 Epoch : 6/10 ... Step : 5530 ... d_loss : 0.1436138153076172 ... g_loss : 3.7537810802459717 Epoch : 6/10 ... Step : 5540 ... d_loss : 0.19169211387634277 ... g_loss : 3.7784411907196045 Epoch : 6/10 ... Step : 5550 ... d_loss : 0.09649496525526047 ... g_loss : 3.745152235031128 Epoch : 6/10 ... Step : 5560 ... d_loss : 0.28526753187179565 ... g_loss : 5.599993705749512 Epoch : 6/10 ... Step : 5570 ... d_loss : 0.2933739125728607 ... g_loss : 3.6453676223754883 Epoch : 6/10 ... Step : 5580 ... d_loss : 0.1903899908065796 ... g_loss : 3.5797648429870605 Epoch : 6/10 ... Step : 5590 ... d_loss : 0.02792230248451233 ... g_loss : 5.365738868713379 Epoch : 6/10 ... Step : 5600 ... d_loss : 0.643322765827179 ... g_loss : 3.335613965988159
Epoch : 6/10 ... Step : 5610 ... d_loss : 0.1698286384344101 ... g_loss : 4.398332595825195 Epoch : 6/10 ... Step : 5620 ... d_loss : 0.7261695265769958 ... g_loss : 7.335690975189209 Epoch : 7/10 ... Step : 5630 ... d_loss : 0.15373989939689636 ... g_loss : 4.304743766784668 Epoch : 7/10 ... Step : 5640 ... d_loss : 0.23926445841789246 ... g_loss : 3.3374369144439697 Epoch : 7/10 ... Step : 5650 ... d_loss : 0.13823336362838745 ... g_loss : 4.02794075012207 Epoch : 7/10 ... Step : 5660 ... d_loss : 0.21189354360103607 ... g_loss : 4.148438453674316 Epoch : 7/10 ... Step : 5670 ... d_loss : 0.8443697690963745 ... g_loss : 3.2465686798095703 Epoch : 7/10 ... Step : 5680 ... d_loss : 1.0965341329574585 ... g_loss : 5.705691337585449 Epoch : 7/10 ... Step : 5690 ... d_loss : 0.5233832597732544 ... g_loss : 2.205422878265381 Epoch : 7/10 ... Step : 5700 ... d_loss : 1.318768858909607 ... g_loss : 2.6436266899108887
Epoch : 7/10 ... Step : 5710 ... d_loss : 0.20642001926898956 ... g_loss : 3.516324281692505 Epoch : 7/10 ... Step : 5720 ... d_loss : 0.3441249430179596 ... g_loss : 4.118350028991699 Epoch : 7/10 ... Step : 5730 ... d_loss : 0.4139905571937561 ... g_loss : 2.393380641937256 Epoch : 7/10 ... Step : 5740 ... d_loss : 0.20899124443531036 ... g_loss : 3.444350242614746 Epoch : 7/10 ... Step : 5750 ... d_loss : 0.14398574829101562 ... g_loss : 4.7330827713012695 Epoch : 7/10 ... Step : 5760 ... d_loss : 1.5069782733917236 ... g_loss : 0.5188373327255249 Epoch : 7/10 ... Step : 5770 ... d_loss : 0.28799283504486084 ... g_loss : 4.296754360198975 Epoch : 7/10 ... Step : 5780 ... d_loss : 0.28413158655166626 ... g_loss : 5.60566520690918 Epoch : 7/10 ... Step : 5790 ... d_loss : 0.3405107855796814 ... g_loss : 4.908329963684082 Epoch : 7/10 ... Step : 5800 ... d_loss : 0.1432686150074005 ... g_loss : 4.765865325927734
Epoch : 7/10 ... Step : 5810 ... d_loss : 0.09705126285552979 ... g_loss : 4.733290195465088 Epoch : 7/10 ... Step : 5820 ... d_loss : 0.024392029270529747 ... g_loss : 5.417802333831787 Epoch : 7/10 ... Step : 5830 ... d_loss : 0.06792791187763214 ... g_loss : 4.474757194519043 Epoch : 7/10 ... Step : 5840 ... d_loss : 0.023117076605558395 ... g_loss : 6.083377361297607 Epoch : 7/10 ... Step : 5850 ... d_loss : 0.0818248763680458 ... g_loss : 3.9261412620544434 Epoch : 7/10 ... Step : 5860 ... d_loss : 0.06720179319381714 ... g_loss : 5.425423622131348 Epoch : 7/10 ... Step : 5870 ... d_loss : 0.05604139342904091 ... g_loss : 4.810156345367432 Epoch : 7/10 ... Step : 5880 ... d_loss : 0.052974678575992584 ... g_loss : 5.684176445007324 Epoch : 7/10 ... Step : 5890 ... d_loss : 0.05421844869852066 ... g_loss : 5.290950298309326 Epoch : 7/10 ... Step : 5900 ... d_loss : 0.050932493060827255 ... g_loss : 5.472050666809082
Epoch : 7/10 ... Step : 5910 ... d_loss : 0.2578391134738922 ... g_loss : 4.01547384262085 Epoch : 7/10 ... Step : 5920 ... d_loss : 0.05760490894317627 ... g_loss : 6.1173481941223145 Epoch : 7/10 ... Step : 5930 ... d_loss : 0.017367437481880188 ... g_loss : 6.17891263961792 Epoch : 7/10 ... Step : 5940 ... d_loss : 0.06722541898488998 ... g_loss : 4.321187496185303 Epoch : 7/10 ... Step : 5950 ... d_loss : 0.00888279639184475 ... g_loss : 6.878222465515137 Epoch : 7/10 ... Step : 5960 ... d_loss : 0.3073692321777344 ... g_loss : 4.851172924041748 Epoch : 7/10 ... Step : 5970 ... d_loss : 0.7359642386436462 ... g_loss : 1.344926118850708 Epoch : 7/10 ... Step : 5980 ... d_loss : 0.3477780818939209 ... g_loss : 5.732503414154053 Epoch : 7/10 ... Step : 5990 ... d_loss : 0.23763659596443176 ... g_loss : 4.6599016189575195 Epoch : 7/10 ... Step : 6000 ... d_loss : 0.2830021381378174 ... g_loss : 3.130481243133545
Epoch : 7/10 ... Step : 6010 ... d_loss : 0.06094885244965553 ... g_loss : 5.597577095031738 Epoch : 7/10 ... Step : 6020 ... d_loss : 0.07582234591245651 ... g_loss : 8.09333324432373 Epoch : 7/10 ... Step : 6030 ... d_loss : 0.037001676857471466 ... g_loss : 5.640501976013184 Epoch : 7/10 ... Step : 6040 ... d_loss : 0.26422977447509766 ... g_loss : 2.784025192260742 Epoch : 7/10 ... Step : 6050 ... d_loss : 0.06802453100681305 ... g_loss : 5.803046226501465 Epoch : 7/10 ... Step : 6060 ... d_loss : 0.10619205236434937 ... g_loss : 5.094736099243164 Epoch : 7/10 ... Step : 6070 ... d_loss : 0.03288344293832779 ... g_loss : 5.825798988342285 Epoch : 7/10 ... Step : 6080 ... d_loss : 0.734998881816864 ... g_loss : 5.750164985656738 Epoch : 7/10 ... Step : 6090 ... d_loss : 0.27672114968299866 ... g_loss : 4.256410121917725 Epoch : 7/10 ... Step : 6100 ... d_loss : 0.03757379576563835 ... g_loss : 6.92812442779541
Epoch : 7/10 ... Step : 6110 ... d_loss : 0.03930037468671799 ... g_loss : 7.1726555824279785 Epoch : 7/10 ... Step : 6120 ... d_loss : 0.09523677080869675 ... g_loss : 5.461270809173584 Epoch : 7/10 ... Step : 6130 ... d_loss : 0.011306834407150745 ... g_loss : 6.8014373779296875 Epoch : 7/10 ... Step : 6140 ... d_loss : 0.35355842113494873 ... g_loss : 4.9966535568237305 Epoch : 7/10 ... Step : 6150 ... d_loss : 0.11655257642269135 ... g_loss : 4.922103404998779 Epoch : 7/10 ... Step : 6160 ... d_loss : 2.119551658630371 ... g_loss : 8.018935203552246 Epoch : 7/10 ... Step : 6170 ... d_loss : 0.4587033987045288 ... g_loss : 5.309537887573242 Epoch : 7/10 ... Step : 6180 ... d_loss : 0.2686750292778015 ... g_loss : 6.158562660217285 Epoch : 7/10 ... Step : 6190 ... d_loss : 0.13235944509506226 ... g_loss : 5.110962390899658 Epoch : 7/10 ... Step : 6200 ... d_loss : 0.35041484236717224 ... g_loss : 7.970481872558594
Epoch : 7/10 ... Step : 6210 ... d_loss : 1.358044981956482 ... g_loss : 7.479506969451904 Epoch : 7/10 ... Step : 6220 ... d_loss : 3.398406505584717 ... g_loss : 6.491769313812256 Epoch : 7/10 ... Step : 6230 ... d_loss : 3.1881232261657715 ... g_loss : 1.1629443168640137 Epoch : 7/10 ... Step : 6240 ... d_loss : 0.2421770542860031 ... g_loss : 4.137707710266113 Epoch : 7/10 ... Step : 6250 ... d_loss : 0.765205979347229 ... g_loss : 2.1890127658843994 Epoch : 7/10 ... Step : 6260 ... d_loss : 0.20210902392864227 ... g_loss : 4.076770782470703 Epoch : 7/10 ... Step : 6270 ... d_loss : 0.12342052161693573 ... g_loss : 4.948429107666016 Epoch : 7/10 ... Step : 6280 ... d_loss : 0.640880286693573 ... g_loss : 2.6560516357421875 Epoch : 7/10 ... Step : 6290 ... d_loss : 2.6175782680511475 ... g_loss : 2.9400060176849365 Epoch : 7/10 ... Step : 6300 ... d_loss : 0.3216474652290344 ... g_loss : 3.462750196456909
Epoch : 7/10 ... Step : 6310 ... d_loss : 0.4823201298713684 ... g_loss : 6.00891637802124 Epoch : 7/10 ... Step : 6320 ... d_loss : 0.16711056232452393 ... g_loss : 4.006435394287109 Epoch : 7/10 ... Step : 6330 ... d_loss : 0.11867885291576385 ... g_loss : 4.324739456176758 Epoch : 7/10 ... Step : 6340 ... d_loss : 1.550455093383789 ... g_loss : 2.053895950317383 Epoch : 7/10 ... Step : 6350 ... d_loss : 0.3158123791217804 ... g_loss : 4.504036903381348 Epoch : 7/10 ... Step : 6360 ... d_loss : 0.1390066295862198 ... g_loss : 5.162228107452393 Epoch : 7/10 ... Step : 6370 ... d_loss : 0.530299961566925 ... g_loss : 4.818005561828613 Epoch : 7/10 ... Step : 6380 ... d_loss : 0.19716519117355347 ... g_loss : 4.552743911743164 Epoch : 7/10 ... Step : 6390 ... d_loss : 1.4779757261276245 ... g_loss : 1.4733836650848389 Epoch : 7/10 ... Step : 6400 ... d_loss : 0.5511246919631958 ... g_loss : 3.4362425804138184
Epoch : 7/10 ... Step : 6410 ... d_loss : 0.9691775441169739 ... g_loss : 4.249008655548096 Epoch : 7/10 ... Step : 6420 ... d_loss : 0.38596871495246887 ... g_loss : 2.709530830383301 Epoch : 7/10 ... Step : 6430 ... d_loss : 0.6735803484916687 ... g_loss : 3.5812883377075195 Epoch : 7/10 ... Step : 6440 ... d_loss : 0.5962095260620117 ... g_loss : 2.9127421379089355 Epoch : 7/10 ... Step : 6450 ... d_loss : 0.756502628326416 ... g_loss : 2.634072780609131 Epoch : 7/10 ... Step : 6460 ... d_loss : 0.08910056203603745 ... g_loss : 4.292091369628906 Epoch : 7/10 ... Step : 6470 ... d_loss : 0.5694804191589355 ... g_loss : 4.610944747924805 Epoch : 7/10 ... Step : 6480 ... d_loss : 1.6615432500839233 ... g_loss : 0.9346472024917603 Epoch : 7/10 ... Step : 6490 ... d_loss : 0.5132297277450562 ... g_loss : 3.209324598312378 Epoch : 7/10 ... Step : 6500 ... d_loss : 0.39844682812690735 ... g_loss : 4.623481273651123
Epoch : 7/10 ... Step : 6510 ... d_loss : 0.4373283386230469 ... g_loss : 5.070298194885254 Epoch : 7/10 ... Step : 6520 ... d_loss : 0.06625533103942871 ... g_loss : 5.400296688079834 Epoch : 7/10 ... Step : 6530 ... d_loss : 0.2353183776140213 ... g_loss : 3.4728927612304688 Epoch : 7/10 ... Step : 6540 ... d_loss : 0.36728623509407043 ... g_loss : 5.988136291503906 Epoch : 7/10 ... Step : 6550 ... d_loss : 1.7023541927337646 ... g_loss : 0.8822206258773804 Epoch : 8/10 ... Step : 6560 ... d_loss : 3.366044282913208 ... g_loss : 1.4294378757476807 Epoch : 8/10 ... Step : 6570 ... d_loss : 0.352583646774292 ... g_loss : 3.8942372798919678 Epoch : 8/10 ... Step : 6580 ... d_loss : 0.2677851617336273 ... g_loss : 4.545865058898926 Epoch : 8/10 ... Step : 6590 ... d_loss : 0.3171163499355316 ... g_loss : 3.2212038040161133 Epoch : 8/10 ... Step : 6600 ... d_loss : 0.11396455764770508 ... g_loss : 4.70059871673584
Epoch : 8/10 ... Step : 6610 ... d_loss : 0.2825580835342407 ... g_loss : 4.3478522300720215 Epoch : 8/10 ... Step : 6620 ... d_loss : 0.18152441084384918 ... g_loss : 3.9770336151123047 Epoch : 8/10 ... Step : 6630 ... d_loss : 0.1496727466583252 ... g_loss : 4.304780960083008 Epoch : 8/10 ... Step : 6640 ... d_loss : 0.07258494198322296 ... g_loss : 4.881650447845459 Epoch : 8/10 ... Step : 6650 ... d_loss : 0.25381404161453247 ... g_loss : 3.9052534103393555 Epoch : 8/10 ... Step : 6660 ... d_loss : 0.14679190516471863 ... g_loss : 5.875266075134277 Epoch : 8/10 ... Step : 6670 ... d_loss : 0.1464974284172058 ... g_loss : 4.7801361083984375 Epoch : 8/10 ... Step : 6680 ... d_loss : 0.1206500381231308 ... g_loss : 5.142016410827637 Epoch : 8/10 ... Step : 6690 ... d_loss : 0.027021897956728935 ... g_loss : 5.464584827423096 Epoch : 8/10 ... Step : 6700 ... d_loss : 0.08663848787546158 ... g_loss : 5.051026344299316
Epoch : 8/10 ... Step : 6710 ... d_loss : 0.08159652352333069 ... g_loss : 4.1985578536987305 Epoch : 8/10 ... Step : 6720 ... d_loss : 0.038410499691963196 ... g_loss : 4.356365203857422 Epoch : 8/10 ... Step : 6730 ... d_loss : 0.02338516153395176 ... g_loss : 5.03650426864624 Epoch : 8/10 ... Step : 6740 ... d_loss : 0.021156784147024155 ... g_loss : 5.135041236877441 Epoch : 8/10 ... Step : 6750 ... d_loss : 0.034728117287158966 ... g_loss : 5.049289703369141 Epoch : 8/10 ... Step : 6760 ... d_loss : 0.06462741643190384 ... g_loss : 4.336012840270996 Epoch : 8/10 ... Step : 6770 ... d_loss : 0.11665989458560944 ... g_loss : 5.073731422424316 Epoch : 8/10 ... Step : 6780 ... d_loss : 0.010741222649812698 ... g_loss : 5.780190467834473 Epoch : 8/10 ... Step : 6790 ... d_loss : 0.03165031969547272 ... g_loss : 5.703655242919922 Epoch : 8/10 ... Step : 6800 ... d_loss : 0.015326099470257759 ... g_loss : 6.9950032234191895
Epoch : 8/10 ... Step : 6810 ... d_loss : 0.1616724133491516 ... g_loss : 4.973468780517578 Epoch : 8/10 ... Step : 6820 ... d_loss : 1.4707207679748535 ... g_loss : 11.809415817260742 Epoch : 8/10 ... Step : 6830 ... d_loss : 0.5445502996444702 ... g_loss : 4.355686187744141 Epoch : 8/10 ... Step : 6840 ... d_loss : 0.5169481039047241 ... g_loss : 3.2315683364868164 Epoch : 8/10 ... Step : 6850 ... d_loss : 0.13557250797748566 ... g_loss : 4.665370941162109 Epoch : 8/10 ... Step : 6860 ... d_loss : 0.06725112348794937 ... g_loss : 4.579063415527344 Epoch : 8/10 ... Step : 6870 ... d_loss : 0.09523981064558029 ... g_loss : 4.794229984283447 Epoch : 8/10 ... Step : 6880 ... d_loss : 0.10349870473146439 ... g_loss : 4.775876045227051 Epoch : 8/10 ... Step : 6890 ... d_loss : 0.04582969471812248 ... g_loss : 5.671006679534912 Epoch : 8/10 ... Step : 6900 ... d_loss : 0.02967769280076027 ... g_loss : 6.1418137550354
Epoch : 8/10 ... Step : 6910 ... d_loss : 0.045884523540735245 ... g_loss : 5.242278575897217 Epoch : 8/10 ... Step : 6920 ... d_loss : 0.14952175319194794 ... g_loss : 3.526087760925293 Epoch : 8/10 ... Step : 6930 ... d_loss : 0.014486514031887054 ... g_loss : 6.4348530769348145 Epoch : 8/10 ... Step : 6940 ... d_loss : 0.019009359180927277 ... g_loss : 5.614012718200684 Epoch : 8/10 ... Step : 6950 ... d_loss : 0.025204889476299286 ... g_loss : 5.1401190757751465 Epoch : 8/10 ... Step : 6960 ... d_loss : 0.07412385940551758 ... g_loss : 4.441115379333496 Epoch : 8/10 ... Step : 6970 ... d_loss : 0.007435818202793598 ... g_loss : 6.884222030639648 Epoch : 8/10 ... Step : 6980 ... d_loss : 3.8511757850646973 ... g_loss : 13.737140655517578 Epoch : 8/10 ... Step : 6990 ... d_loss : 0.8307238817214966 ... g_loss : 1.502221941947937 Epoch : 8/10 ... Step : 7000 ... d_loss : 0.8426401615142822 ... g_loss : 2.485095977783203
Epoch : 8/10 ... Step : 7010 ... d_loss : 0.3740556240081787 ... g_loss : 4.561698913574219 Epoch : 8/10 ... Step : 7020 ... d_loss : 0.8775414228439331 ... g_loss : 2.4035634994506836 Epoch : 8/10 ... Step : 7030 ... d_loss : 0.2238444685935974 ... g_loss : 3.7813806533813477 Epoch : 8/10 ... Step : 7040 ... d_loss : 0.15043029189109802 ... g_loss : 4.1201019287109375 Epoch : 8/10 ... Step : 7050 ... d_loss : 0.14967307448387146 ... g_loss : 4.990384578704834 Epoch : 8/10 ... Step : 7060 ... d_loss : 0.3575742244720459 ... g_loss : 4.025999069213867 Epoch : 8/10 ... Step : 7070 ... d_loss : 0.5007606744766235 ... g_loss : 3.53810453414917 Epoch : 8/10 ... Step : 7080 ... d_loss : 0.1674249917268753 ... g_loss : 4.546578407287598 Epoch : 8/10 ... Step : 7090 ... d_loss : 0.4031786024570465 ... g_loss : 5.372525691986084 Epoch : 8/10 ... Step : 7100 ... d_loss : 0.5394906997680664 ... g_loss : 3.9126834869384766
Epoch : 8/10 ... Step : 7110 ... d_loss : 0.2254367619752884 ... g_loss : 5.033327102661133 Epoch : 8/10 ... Step : 7120 ... d_loss : 0.1136808916926384 ... g_loss : 4.547408103942871 Epoch : 8/10 ... Step : 7130 ... d_loss : 0.17243440449237823 ... g_loss : 5.028343200683594 Epoch : 8/10 ... Step : 7140 ... d_loss : 0.028054947033524513 ... g_loss : 5.053067684173584 Epoch : 8/10 ... Step : 7150 ... d_loss : 0.03670808672904968 ... g_loss : 5.533154487609863 Epoch : 8/10 ... Step : 7160 ... d_loss : 0.012681552208960056 ... g_loss : 6.2691521644592285 Epoch : 8/10 ... Step : 7170 ... d_loss : 0.013751138933002949 ... g_loss : 6.159887313842773 Epoch : 8/10 ... Step : 7180 ... d_loss : 0.02221427857875824 ... g_loss : 5.624387741088867 Epoch : 8/10 ... Step : 7190 ... d_loss : 0.3049319386482239 ... g_loss : 5.927455902099609 Epoch : 8/10 ... Step : 7200 ... d_loss : 0.19604678452014923 ... g_loss : 5.720058441162109
Epoch : 8/10 ... Step : 7210 ... d_loss : 0.02540198341012001 ... g_loss : 5.118842124938965 Epoch : 8/10 ... Step : 7220 ... d_loss : 0.042691946029663086 ... g_loss : 5.58624267578125 Epoch : 8/10 ... Step : 7230 ... d_loss : 0.05953044816851616 ... g_loss : 5.313941478729248 Epoch : 8/10 ... Step : 7240 ... d_loss : 0.12026609480381012 ... g_loss : 5.480389595031738 Epoch : 8/10 ... Step : 7250 ... d_loss : 0.3190840184688568 ... g_loss : 5.618704795837402 Epoch : 8/10 ... Step : 7260 ... d_loss : 1.7932709455490112 ... g_loss : 6.614612102508545 Epoch : 8/10 ... Step : 7270 ... d_loss : 1.8097338676452637 ... g_loss : 7.698023796081543 Epoch : 8/10 ... Step : 7280 ... d_loss : 0.7624855637550354 ... g_loss : 5.123142242431641 Epoch : 8/10 ... Step : 7290 ... d_loss : 0.12528645992279053 ... g_loss : 4.472404479980469 Epoch : 8/10 ... Step : 7300 ... d_loss : 0.5918808579444885 ... g_loss : 4.601323127746582
Epoch : 8/10 ... Step : 7310 ... d_loss : 0.2348114401102066 ... g_loss : 4.63798189163208 Epoch : 8/10 ... Step : 7320 ... d_loss : 1.9421344995498657 ... g_loss : 5.876347541809082 Epoch : 8/10 ... Step : 7330 ... d_loss : 0.8474411964416504 ... g_loss : 1.9068129062652588 Epoch : 8/10 ... Step : 7340 ... d_loss : 0.6478233337402344 ... g_loss : 4.554998397827148 Epoch : 8/10 ... Step : 7350 ... d_loss : 0.10336187481880188 ... g_loss : 4.308013916015625 Epoch : 8/10 ... Step : 7360 ... d_loss : 0.4774428606033325 ... g_loss : 4.705096244812012 Epoch : 8/10 ... Step : 7370 ... d_loss : 0.47260782122612 ... g_loss : 2.137207269668579 Epoch : 8/10 ... Step : 7380 ... d_loss : 1.0686267614364624 ... g_loss : 7.0197038650512695 Epoch : 8/10 ... Step : 7390 ... d_loss : 0.051163557916879654 ... g_loss : 4.877223014831543 Epoch : 8/10 ... Step : 7400 ... d_loss : 0.3599926829338074 ... g_loss : 6.893950462341309
Epoch : 8/10 ... Step : 7410 ... d_loss : 0.8277201056480408 ... g_loss : 1.8116278648376465 Epoch : 8/10 ... Step : 7420 ... d_loss : 1.0832741260528564 ... g_loss : 3.1335697174072266 Epoch : 8/10 ... Step : 7430 ... d_loss : 0.15250447392463684 ... g_loss : 4.343381881713867 Epoch : 8/10 ... Step : 7440 ... d_loss : 0.1266697496175766 ... g_loss : 4.043994903564453 Epoch : 8/10 ... Step : 7450 ... d_loss : 0.3550424873828888 ... g_loss : 3.431610345840454 Epoch : 8/10 ... Step : 7460 ... d_loss : 0.04729665443301201 ... g_loss : 5.314352989196777 Epoch : 8/10 ... Step : 7470 ... d_loss : 0.03618132323026657 ... g_loss : 4.693302154541016 Epoch : 8/10 ... Step : 7480 ... d_loss : 0.1616870015859604 ... g_loss : 4.094493865966797 Epoch : 8/10 ... Step : 7490 ... d_loss : 0.25787240266799927 ... g_loss : 4.60886812210083 Epoch : 9/10 ... Step : 7500 ... d_loss : 0.3818618357181549 ... g_loss : 2.0849928855895996
Epoch : 9/10 ... Step : 7510 ... d_loss : 0.4042336344718933 ... g_loss : 2.466742753982544 Epoch : 9/10 ... Step : 7520 ... d_loss : 0.8818703889846802 ... g_loss : 2.6488864421844482 Epoch : 9/10 ... Step : 7530 ... d_loss : 0.40899068117141724 ... g_loss : 4.628652572631836 Epoch : 9/10 ... Step : 7540 ... d_loss : 0.8671266436576843 ... g_loss : 1.91530442237854 Epoch : 9/10 ... Step : 7550 ... d_loss : 0.18957504630088806 ... g_loss : 6.274324417114258 Epoch : 9/10 ... Step : 7560 ... d_loss : 0.09760737419128418 ... g_loss : 5.155766010284424 Epoch : 9/10 ... Step : 7570 ... d_loss : 0.08985822647809982 ... g_loss : 4.995553970336914 Epoch : 9/10 ... Step : 7580 ... d_loss : 0.2858957052230835 ... g_loss : 6.214184761047363 Epoch : 9/10 ... Step : 7590 ... d_loss : 0.1089886873960495 ... g_loss : 4.853036880493164 Epoch : 9/10 ... Step : 7600 ... d_loss : 0.0636819452047348 ... g_loss : 4.879247188568115
Epoch : 9/10 ... Step : 7610 ... d_loss : 0.2514427602291107 ... g_loss : 4.507157325744629 Epoch : 9/10 ... Step : 7620 ... d_loss : 0.08562549948692322 ... g_loss : 5.354313373565674 Epoch : 9/10 ... Step : 7630 ... d_loss : 0.06112661212682724 ... g_loss : 4.217367172241211 Epoch : 9/10 ... Step : 7640 ... d_loss : 3.355363368988037 ... g_loss : 8.952975273132324 Epoch : 9/10 ... Step : 7650 ... d_loss : 0.5968196392059326 ... g_loss : 1.8571504354476929 Epoch : 9/10 ... Step : 7660 ... d_loss : 2.4099020957946777 ... g_loss : 10.840856552124023 Epoch : 9/10 ... Step : 7670 ... d_loss : 0.32325902581214905 ... g_loss : 4.644920349121094 Epoch : 9/10 ... Step : 7680 ... d_loss : 0.34181463718414307 ... g_loss : 4.072692394256592 Epoch : 9/10 ... Step : 7690 ... d_loss : 0.5349408984184265 ... g_loss : 8.30923080444336 Epoch : 9/10 ... Step : 7700 ... d_loss : 1.347314476966858 ... g_loss : 2.4087533950805664
Epoch : 9/10 ... Step : 7710 ... d_loss : 3.1922595500946045 ... g_loss : 0.929093062877655 Epoch : 9/10 ... Step : 7720 ... d_loss : 0.3270491659641266 ... g_loss : 3.3716976642608643 Epoch : 9/10 ... Step : 7730 ... d_loss : 0.11915597319602966 ... g_loss : 4.264631271362305 Epoch : 9/10 ... Step : 7740 ... d_loss : 0.19134655594825745 ... g_loss : 3.7829418182373047 Epoch : 9/10 ... Step : 7750 ... d_loss : 0.12623992562294006 ... g_loss : 5.097655296325684 Epoch : 9/10 ... Step : 7760 ... d_loss : 0.04131944477558136 ... g_loss : 4.219832897186279 Epoch : 9/10 ... Step : 7770 ... d_loss : 0.24725347757339478 ... g_loss : 3.8838419914245605 Epoch : 9/10 ... Step : 7780 ... d_loss : 0.20176473259925842 ... g_loss : 3.9456818103790283 Epoch : 9/10 ... Step : 7790 ... d_loss : 0.2410006821155548 ... g_loss : 3.7313034534454346 Epoch : 9/10 ... Step : 7800 ... d_loss : 0.7769963145256042 ... g_loss : 3.09781551361084
Epoch : 9/10 ... Step : 7810 ... d_loss : 0.09105034172534943 ... g_loss : 5.858297348022461 Epoch : 9/10 ... Step : 7820 ... d_loss : 1.287761926651001 ... g_loss : 8.637648582458496 Epoch : 9/10 ... Step : 7830 ... d_loss : 0.25370925664901733 ... g_loss : 3.1233646869659424 Epoch : 9/10 ... Step : 7840 ... d_loss : 0.12436895817518234 ... g_loss : 4.027643203735352 Epoch : 9/10 ... Step : 7850 ... d_loss : 0.42842575907707214 ... g_loss : 6.7212114334106445 Epoch : 9/10 ... Step : 7860 ... d_loss : 0.02553064003586769 ... g_loss : 5.959301948547363 Epoch : 9/10 ... Step : 7870 ... d_loss : 0.07257004082202911 ... g_loss : 4.993178367614746 Epoch : 9/10 ... Step : 7880 ... d_loss : 0.12290985882282257 ... g_loss : 5.682604789733887 Epoch : 9/10 ... Step : 7890 ... d_loss : 0.11228688061237335 ... g_loss : 5.093951225280762 Epoch : 9/10 ... Step : 7900 ... d_loss : 0.02030842751264572 ... g_loss : 6.039531230926514
Epoch : 9/10 ... Step : 7910 ... d_loss : 0.3373602628707886 ... g_loss : 2.8591480255126953 Epoch : 9/10 ... Step : 7920 ... d_loss : 2.013003349304199 ... g_loss : 7.242922782897949 Epoch : 9/10 ... Step : 7930 ... d_loss : 1.0448851585388184 ... g_loss : 5.3938446044921875 Epoch : 9/10 ... Step : 7940 ... d_loss : 0.1395171880722046 ... g_loss : 4.779216766357422 Epoch : 9/10 ... Step : 7950 ... d_loss : 1.1888431310653687 ... g_loss : 1.7881970405578613 Epoch : 9/10 ... Step : 7960 ... d_loss : 0.39960333704948425 ... g_loss : 5.675043106079102 Epoch : 9/10 ... Step : 7970 ... d_loss : 0.10337439924478531 ... g_loss : 5.381010055541992 Epoch : 9/10 ... Step : 7980 ... d_loss : 0.06035660207271576 ... g_loss : 4.8448662757873535 Epoch : 9/10 ... Step : 7990 ... d_loss : 0.04484140872955322 ... g_loss : 5.660824298858643 Epoch : 9/10 ... Step : 8000 ... d_loss : 0.11053695529699326 ... g_loss : 5.1335530281066895
Epoch : 9/10 ... Step : 8010 ... d_loss : 0.19868412613868713 ... g_loss : 4.539027214050293 Epoch : 9/10 ... Step : 8020 ... d_loss : 0.23338942229747772 ... g_loss : 3.417448043823242 Epoch : 9/10 ... Step : 8030 ... d_loss : 0.0840044766664505 ... g_loss : 4.930631160736084 Epoch : 9/10 ... Step : 8040 ... d_loss : 0.12576743960380554 ... g_loss : 4.844372749328613 Epoch : 9/10 ... Step : 8050 ... d_loss : 0.23476581275463104 ... g_loss : 5.02703857421875 Epoch : 9/10 ... Step : 8060 ... d_loss : 0.06928572803735733 ... g_loss : 5.930112838745117 Epoch : 9/10 ... Step : 8070 ... d_loss : 0.3391801714897156 ... g_loss : 8.504150390625 Epoch : 9/10 ... Step : 8080 ... d_loss : 0.9143259525299072 ... g_loss : 4.881326675415039 Epoch : 9/10 ... Step : 8090 ... d_loss : 0.5843212604522705 ... g_loss : 3.632030725479126 Epoch : 9/10 ... Step : 8100 ... d_loss : 0.4431460499763489 ... g_loss : 2.623769760131836
Epoch : 9/10 ... Step : 8110 ... d_loss : 0.39128702878952026 ... g_loss : 3.0341410636901855 Epoch : 9/10 ... Step : 8120 ... d_loss : 0.45025867223739624 ... g_loss : 4.568940162658691 Epoch : 9/10 ... Step : 8130 ... d_loss : 0.07877494394779205 ... g_loss : 4.415193557739258 Epoch : 9/10 ... Step : 8140 ... d_loss : 0.1930781900882721 ... g_loss : 4.3216047286987305 Epoch : 9/10 ... Step : 8150 ... d_loss : 0.06701169908046722 ... g_loss : 4.526359558105469 Epoch : 9/10 ... Step : 8160 ... d_loss : 0.44790351390838623 ... g_loss : 6.914380073547363 Epoch : 9/10 ... Step : 8170 ... d_loss : 0.7383239269256592 ... g_loss : 3.1212944984436035 Epoch : 9/10 ... Step : 8180 ... d_loss : 1.2359864711761475 ... g_loss : 0.708024263381958 Epoch : 9/10 ... Step : 8190 ... d_loss : 0.44317877292633057 ... g_loss : 5.607193946838379 Epoch : 9/10 ... Step : 8200 ... d_loss : 0.0683806911110878 ... g_loss : 4.23142147064209
Epoch : 9/10 ... Step : 8210 ... d_loss : 0.17106449604034424 ... g_loss : 4.1974053382873535 Epoch : 9/10 ... Step : 8220 ... d_loss : 0.5314615368843079 ... g_loss : 4.158939361572266 Epoch : 9/10 ... Step : 8230 ... d_loss : 0.46965786814689636 ... g_loss : 3.441784143447876 Epoch : 9/10 ... Step : 8240 ... d_loss : 0.22155646979808807 ... g_loss : 5.061676025390625 Epoch : 9/10 ... Step : 8250 ... d_loss : 0.447399765253067 ... g_loss : 10.114240646362305 Epoch : 9/10 ... Step : 8260 ... d_loss : 0.7012417912483215 ... g_loss : 3.553061008453369 Epoch : 9/10 ... Step : 8270 ... d_loss : 0.32708972692489624 ... g_loss : 2.9488325119018555 Epoch : 9/10 ... Step : 8280 ... d_loss : 1.1856789588928223 ... g_loss : 5.217775821685791 Epoch : 9/10 ... Step : 8290 ... d_loss : 0.6705029010772705 ... g_loss : 2.3119425773620605 Epoch : 9/10 ... Step : 8300 ... d_loss : 0.04415960982441902 ... g_loss : 5.458360195159912
Epoch : 9/10 ... Step : 8310 ... d_loss : 0.5358302593231201 ... g_loss : 2.540712833404541 Epoch : 9/10 ... Step : 8320 ... d_loss : 0.08369970321655273 ... g_loss : 4.874293327331543 Epoch : 9/10 ... Step : 8330 ... d_loss : 0.06307505071163177 ... g_loss : 5.895158767700195 Epoch : 9/10 ... Step : 8340 ... d_loss : 0.05525326728820801 ... g_loss : 5.846868515014648 Epoch : 9/10 ... Step : 8350 ... d_loss : 0.11104336380958557 ... g_loss : 4.5625386238098145 Epoch : 9/10 ... Step : 8360 ... d_loss : 0.17994453012943268 ... g_loss : 4.300849914550781 Epoch : 9/10 ... Step : 8370 ... d_loss : 0.06649880111217499 ... g_loss : 5.582294464111328 Epoch : 9/10 ... Step : 8380 ... d_loss : 0.189071387052536 ... g_loss : 4.384601593017578 Epoch : 9/10 ... Step : 8390 ... d_loss : 0.24489636719226837 ... g_loss : 6.95518159866333 Epoch : 9/10 ... Step : 8400 ... d_loss : 0.032989244908094406 ... g_loss : 4.771982669830322
Epoch : 9/10 ... Step : 8410 ... d_loss : 0.2812952399253845 ... g_loss : 6.683961868286133 Epoch : 9/10 ... Step : 8420 ... d_loss : 0.013032998889684677 ... g_loss : 6.033252716064453 Epoch : 9/10 ... Step : 8430 ... d_loss : 0.025741275399923325 ... g_loss : 5.687305927276611 Epoch : 10/10 ... Step : 8440 ... d_loss : 0.025143297389149666 ... g_loss : 6.795241355895996 Epoch : 10/10 ... Step : 8450 ... d_loss : 0.10931483656167984 ... g_loss : 4.972301483154297 Epoch : 10/10 ... Step : 8460 ... d_loss : 0.06018202751874924 ... g_loss : 5.99501895904541 Epoch : 10/10 ... Step : 8470 ... d_loss : 0.01033933274447918 ... g_loss : 6.130795478820801 Epoch : 10/10 ... Step : 8480 ... d_loss : 0.026147909462451935 ... g_loss : 5.659487724304199 Epoch : 10/10 ... Step : 8490 ... d_loss : 0.067345030605793 ... g_loss : 6.12630558013916 Epoch : 10/10 ... Step : 8500 ... d_loss : 0.0075219059363007545 ... g_loss : 7.363768100738525
Epoch : 10/10 ... Step : 8510 ... d_loss : 0.012602985836565495 ... g_loss : 7.851783752441406 Epoch : 10/10 ... Step : 8520 ... d_loss : 0.01894901692867279 ... g_loss : 5.627358436584473 Epoch : 10/10 ... Step : 8530 ... d_loss : 0.014539474621415138 ... g_loss : 5.615194797515869 Epoch : 10/10 ... Step : 8540 ... d_loss : 0.2479589432477951 ... g_loss : 2.4038538932800293 Epoch : 10/10 ... Step : 8550 ... d_loss : 0.035542309284210205 ... g_loss : 5.237662315368652 Epoch : 10/10 ... Step : 8560 ... d_loss : 0.02361782267689705 ... g_loss : 5.962926864624023 Epoch : 10/10 ... Step : 8570 ... d_loss : 0.18749170005321503 ... g_loss : 7.021846771240234 Epoch : 10/10 ... Step : 8580 ... d_loss : 0.08598189055919647 ... g_loss : 5.823880195617676 Epoch : 10/10 ... Step : 8590 ... d_loss : 0.3220805525779724 ... g_loss : 4.317268371582031 Epoch : 10/10 ... Step : 8600 ... d_loss : 0.263752281665802 ... g_loss : 4.75369930267334
Epoch : 10/10 ... Step : 8610 ... d_loss : 0.09312573075294495 ... g_loss : 6.443691730499268 Epoch : 10/10 ... Step : 8620 ... d_loss : 4.208970546722412 ... g_loss : 3.112842082977295 Epoch : 10/10 ... Step : 8630 ... d_loss : 0.24016140401363373 ... g_loss : 5.050767421722412 Epoch : 10/10 ... Step : 8640 ... d_loss : 0.4369646906852722 ... g_loss : 5.350590705871582 Epoch : 10/10 ... Step : 8650 ... d_loss : 1.0843929052352905 ... g_loss : 4.901951313018799 Epoch : 10/10 ... Step : 8660 ... d_loss : 0.5985576510429382 ... g_loss : 1.9347938299179077 Epoch : 10/10 ... Step : 8670 ... d_loss : 0.6615407466888428 ... g_loss : 5.1403937339782715 Epoch : 10/10 ... Step : 8680 ... d_loss : 0.6490510702133179 ... g_loss : 7.712409019470215 Epoch : 10/10 ... Step : 8690 ... d_loss : 0.5330877304077148 ... g_loss : 8.1532564163208 Epoch : 10/10 ... Step : 8700 ... d_loss : 0.1083986759185791 ... g_loss : 5.1294755935668945
Epoch : 10/10 ... Step : 8710 ... d_loss : 0.12686023116111755 ... g_loss : 5.848816871643066 Epoch : 10/10 ... Step : 8720 ... d_loss : 1.4600204229354858 ... g_loss : 0.5341081619262695 Epoch : 10/10 ... Step : 8730 ... d_loss : 0.1037520319223404 ... g_loss : 7.496457576751709 Epoch : 10/10 ... Step : 8740 ... d_loss : 0.2748379111289978 ... g_loss : 3.2423577308654785 Epoch : 10/10 ... Step : 8750 ... d_loss : 0.7986506819725037 ... g_loss : 7.523667335510254 Epoch : 10/10 ... Step : 8760 ... d_loss : 0.9881500601768494 ... g_loss : 2.2623472213745117 Epoch : 10/10 ... Step : 8770 ... d_loss : 0.2616402804851532 ... g_loss : 3.4571921825408936 Epoch : 10/10 ... Step : 8780 ... d_loss : 0.5033376216888428 ... g_loss : 4.506999969482422 Epoch : 10/10 ... Step : 8790 ... d_loss : 0.12621194124221802 ... g_loss : 5.516800880432129 Epoch : 10/10 ... Step : 8800 ... d_loss : 0.10757303237915039 ... g_loss : 5.655228614807129
Epoch : 10/10 ... Step : 8810 ... d_loss : 0.37965095043182373 ... g_loss : 5.855498790740967 Epoch : 10/10 ... Step : 8820 ... d_loss : 1.6344645023345947 ... g_loss : 1.31074857711792 Epoch : 10/10 ... Step : 8830 ... d_loss : 1.776843547821045 ... g_loss : 5.052001953125 Epoch : 10/10 ... Step : 8840 ... d_loss : 1.831725835800171 ... g_loss : 0.7961486577987671 Epoch : 10/10 ... Step : 8850 ... d_loss : 0.36329254508018494 ... g_loss : 4.231732368469238 Epoch : 10/10 ... Step : 8860 ... d_loss : 0.3647105395793915 ... g_loss : 4.984014511108398 Epoch : 10/10 ... Step : 8870 ... d_loss : 0.3348284959793091 ... g_loss : 2.8671042919158936 Epoch : 10/10 ... Step : 8880 ... d_loss : 0.13347995281219482 ... g_loss : 4.530256748199463 Epoch : 10/10 ... Step : 8890 ... d_loss : 0.08504369854927063 ... g_loss : 4.775420188903809 Epoch : 10/10 ... Step : 8900 ... d_loss : 0.0735631138086319 ... g_loss : 4.863027572631836
Epoch : 10/10 ... Step : 8910 ... d_loss : 0.5083227753639221 ... g_loss : 8.727542877197266 Epoch : 10/10 ... Step : 8920 ... d_loss : 0.18465644121170044 ... g_loss : 5.157007217407227 Epoch : 10/10 ... Step : 8930 ... d_loss : 0.13928547501564026 ... g_loss : 3.228536605834961 Epoch : 10/10 ... Step : 8940 ... d_loss : 0.1630071997642517 ... g_loss : 5.42637825012207 Epoch : 10/10 ... Step : 8950 ... d_loss : 1.2174274921417236 ... g_loss : 3.7761635780334473 Epoch : 10/10 ... Step : 8960 ... d_loss : 0.5976399183273315 ... g_loss : 3.040825366973877 Epoch : 10/10 ... Step : 8970 ... d_loss : 0.22763469815254211 ... g_loss : 3.2224369049072266 Epoch : 10/10 ... Step : 8980 ... d_loss : 0.649651288986206 ... g_loss : 5.579798698425293 Epoch : 10/10 ... Step : 8990 ... d_loss : 0.03867976367473602 ... g_loss : 5.213484764099121 Epoch : 10/10 ... Step : 9000 ... d_loss : 0.42648762464523315 ... g_loss : 4.431921005249023
Epoch : 10/10 ... Step : 9010 ... d_loss : 0.7176114916801453 ... g_loss : 6.963593482971191 Epoch : 10/10 ... Step : 9020 ... d_loss : 0.5666561126708984 ... g_loss : 4.155869007110596 Epoch : 10/10 ... Step : 9030 ... d_loss : 0.11272989213466644 ... g_loss : 4.999504566192627 Epoch : 10/10 ... Step : 9040 ... d_loss : 0.08841568231582642 ... g_loss : 4.573569297790527 Epoch : 10/10 ... Step : 9050 ... d_loss : 0.1417345553636551 ... g_loss : 4.299032211303711 Epoch : 10/10 ... Step : 9060 ... d_loss : 0.5630269646644592 ... g_loss : 7.504783630371094 Epoch : 10/10 ... Step : 9070 ... d_loss : 0.23506267368793488 ... g_loss : 6.16700553894043 Epoch : 10/10 ... Step : 9080 ... d_loss : 0.028826724737882614 ... g_loss : 5.226724624633789 Epoch : 10/10 ... Step : 9090 ... d_loss : 0.10017924010753632 ... g_loss : 8.412052154541016 Epoch : 10/10 ... Step : 9100 ... d_loss : 0.07008880376815796 ... g_loss : 4.588155746459961
Epoch : 10/10 ... Step : 9110 ... d_loss : 0.016904745250940323 ... g_loss : 6.349428653717041 Epoch : 10/10 ... Step : 9120 ... d_loss : 0.09405761957168579 ... g_loss : 4.58247184753418 Epoch : 10/10 ... Step : 9130 ... d_loss : 0.045009955763816833 ... g_loss : 6.635502815246582 Epoch : 10/10 ... Step : 9140 ... d_loss : 0.03307946026325226 ... g_loss : 5.447530746459961 Epoch : 10/10 ... Step : 9150 ... d_loss : 0.45312929153442383 ... g_loss : 6.178813934326172 Epoch : 10/10 ... Step : 9160 ... d_loss : 0.045109424740076065 ... g_loss : 8.362558364868164 Epoch : 10/10 ... Step : 9170 ... d_loss : 0.062018632888793945 ... g_loss : 5.34347677230835 Epoch : 10/10 ... Step : 9180 ... d_loss : 0.08079024404287338 ... g_loss : 5.815608978271484 Epoch : 10/10 ... Step : 9190 ... d_loss : 0.05467529967427254 ... g_loss : 4.445769309997559 Epoch : 10/10 ... Step : 9200 ... d_loss : 0.19487079977989197 ... g_loss : 6.059539318084717
Epoch : 10/10 ... Step : 9210 ... d_loss : 0.22116905450820923 ... g_loss : 3.861384630203247 Epoch : 10/10 ... Step : 9220 ... d_loss : 1.7499115467071533 ... g_loss : 2.2347054481506348 Epoch : 10/10 ... Step : 9230 ... d_loss : 0.12081797420978546 ... g_loss : 5.437183380126953 Epoch : 10/10 ... Step : 9240 ... d_loss : 0.01917901448905468 ... g_loss : 6.939822196960449 Epoch : 10/10 ... Step : 9250 ... d_loss : 0.10914123058319092 ... g_loss : 5.375083923339844 Epoch : 10/10 ... Step : 9260 ... d_loss : 0.10312144458293915 ... g_loss : 5.889101505279541 Epoch : 10/10 ... Step : 9270 ... d_loss : 0.02201795019209385 ... g_loss : 6.084514617919922 Epoch : 10/10 ... Step : 9280 ... d_loss : 0.07486442476511002 ... g_loss : 5.19714879989624 Epoch : 10/10 ... Step : 9290 ... d_loss : 0.05262728035449982 ... g_loss : 4.99378776550293 Epoch : 10/10 ... Step : 9300 ... d_loss : 0.05909232422709465 ... g_loss : 4.847980976104736
Epoch : 10/10 ... Step : 9310 ... d_loss : 0.03068324364721775 ... g_loss : 5.809871673583984 Epoch : 10/10 ... Step : 9320 ... d_loss : 1.3452600240707397 ... g_loss : 0.7153559923171997 Epoch : 10/10 ... Step : 9330 ... d_loss : 0.6744118928909302 ... g_loss : 8.476202011108398 Epoch : 10/10 ... Step : 9340 ... d_loss : 0.19640986621379852 ... g_loss : 3.3702633380889893 Epoch : 10/10 ... Step : 9350 ... d_loss : 2.9986300468444824 ... g_loss : 1.3983550071716309 Epoch : 10/10 ... Step : 9360 ... d_loss : 0.8579264879226685 ... g_loss : 6.541449069976807 Epoch : 10/10 ... Step : 9370 ... d_loss : 0.028427165001630783 ... g_loss : 7.290179252624512
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 64
z_dim = 200
learning_rate = 1e-3
beta1 = .5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 10
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
Epoch : 1/10 ... Step : 100 ... d_loss : 0.276090532541275 ... g_loss : 7.946306228637695 Epoch : 1/10 ... Step : 200 ... d_loss : 0.4930230379104614 ... g_loss : 7.372992515563965 Epoch : 1/10 ... Step : 300 ... d_loss : 0.5517822504043579 ... g_loss : 4.447853088378906 Epoch : 1/10 ... Step : 400 ... d_loss : 0.7986226081848145 ... g_loss : 2.0185770988464355 Epoch : 1/10 ... Step : 500 ... d_loss : 1.2721617221832275 ... g_loss : 2.4557392597198486 Epoch : 1/10 ... Step : 600 ... d_loss : 0.9766598343849182 ... g_loss : 2.350954055786133 Epoch : 1/10 ... Step : 700 ... d_loss : 1.061157464981079 ... g_loss : 2.4358301162719727 Epoch : 1/10 ... Step : 800 ... d_loss : 1.1672296524047852 ... g_loss : 1.8027163743972778 Epoch : 1/10 ... Step : 900 ... d_loss : 1.6221281290054321 ... g_loss : 3.5671944618225098 Epoch : 1/10 ... Step : 1000 ... d_loss : 1.0373295545578003 ... g_loss : 2.998807907104492
Epoch : 1/10 ... Step : 1100 ... d_loss : 1.1935968399047852 ... g_loss : 1.9688359498977661 Epoch : 1/10 ... Step : 1200 ... d_loss : 1.2537930011749268 ... g_loss : 1.2126582860946655 Epoch : 1/10 ... Step : 1300 ... d_loss : 0.9132339954376221 ... g_loss : 2.275480270385742 Epoch : 1/10 ... Step : 1400 ... d_loss : 1.2980901002883911 ... g_loss : 3.1403751373291016 Epoch : 1/10 ... Step : 1500 ... d_loss : 0.9228848218917847 ... g_loss : 1.241837978363037 Epoch : 1/10 ... Step : 1600 ... d_loss : 1.315888524055481 ... g_loss : 2.451977014541626 Epoch : 1/10 ... Step : 1700 ... d_loss : 0.9177507758140564 ... g_loss : 3.095423698425293 Epoch : 1/10 ... Step : 1800 ... d_loss : 1.277606725692749 ... g_loss : 1.3688812255859375 Epoch : 1/10 ... Step : 1900 ... d_loss : 0.986170768737793 ... g_loss : 1.9964361190795898 Epoch : 1/10 ... Step : 2000 ... d_loss : 1.0518923997879028 ... g_loss : 2.5830845832824707
Epoch : 1/10 ... Step : 2100 ... d_loss : 0.980575442314148 ... g_loss : 1.716047763824463 Epoch : 1/10 ... Step : 2200 ... d_loss : 0.7019271850585938 ... g_loss : 1.8456089496612549 Epoch : 1/10 ... Step : 2300 ... d_loss : 0.7612940073013306 ... g_loss : 2.375460386276245 Epoch : 1/10 ... Step : 2400 ... d_loss : 1.142967700958252 ... g_loss : 1.5365822315216064 Epoch : 1/10 ... Step : 2500 ... d_loss : 0.9833749532699585 ... g_loss : 1.5736494064331055 Epoch : 1/10 ... Step : 2600 ... d_loss : 1.3212558031082153 ... g_loss : 1.7395437955856323 Epoch : 1/10 ... Step : 2700 ... d_loss : 1.0128319263458252 ... g_loss : 1.5958023071289062 Epoch : 1/10 ... Step : 2800 ... d_loss : 1.1938549280166626 ... g_loss : 1.3919132947921753 Epoch : 1/10 ... Step : 2900 ... d_loss : 1.1105329990386963 ... g_loss : 1.8811490535736084 Epoch : 1/10 ... Step : 3000 ... d_loss : 1.0103437900543213 ... g_loss : 1.3165602684020996
Epoch : 1/10 ... Step : 3100 ... d_loss : 0.8804258108139038 ... g_loss : 1.5781946182250977 Epoch : 2/10 ... Step : 3200 ... d_loss : 1.0533196926116943 ... g_loss : 2.0449299812316895 Epoch : 2/10 ... Step : 3300 ... d_loss : 1.0396111011505127 ... g_loss : 1.7734143733978271 Epoch : 2/10 ... Step : 3400 ... d_loss : 1.216396450996399 ... g_loss : 1.3083497285842896 Epoch : 2/10 ... Step : 3500 ... d_loss : 0.8309692144393921 ... g_loss : 2.2075929641723633 Epoch : 2/10 ... Step : 3600 ... d_loss : 0.9828373193740845 ... g_loss : 1.773605465888977 Epoch : 2/10 ... Step : 3700 ... d_loss : 0.9848295450210571 ... g_loss : 3.1024365425109863 Epoch : 2/10 ... Step : 3800 ... d_loss : 0.9401560425758362 ... g_loss : 2.2019243240356445 Epoch : 2/10 ... Step : 3900 ... d_loss : 1.4543629884719849 ... g_loss : 1.047347068786621 Epoch : 2/10 ... Step : 4000 ... d_loss : 0.7480818629264832 ... g_loss : 2.766585350036621
Epoch : 2/10 ... Step : 4100 ... d_loss : 1.1502654552459717 ... g_loss : 1.4453022480010986 Epoch : 2/10 ... Step : 4200 ... d_loss : 1.403666377067566 ... g_loss : 3.304753541946411 Epoch : 2/10 ... Step : 4300 ... d_loss : 1.026075839996338 ... g_loss : 1.3864378929138184 Epoch : 2/10 ... Step : 4400 ... d_loss : 1.0760143995285034 ... g_loss : 2.1319215297698975 Epoch : 2/10 ... Step : 4500 ... d_loss : 1.3108898401260376 ... g_loss : 2.742766857147217 Epoch : 2/10 ... Step : 4600 ... d_loss : 1.506540060043335 ... g_loss : 1.1960229873657227 Epoch : 2/10 ... Step : 4700 ... d_loss : 1.1399314403533936 ... g_loss : 4.008848667144775 Epoch : 2/10 ... Step : 4800 ... d_loss : 0.7025111317634583 ... g_loss : 1.8710358142852783 Epoch : 2/10 ... Step : 4900 ... d_loss : 1.1308298110961914 ... g_loss : 2.3569982051849365 Epoch : 2/10 ... Step : 5000 ... d_loss : 0.6579766273498535 ... g_loss : 2.144453525543213
Epoch : 2/10 ... Step : 5100 ... d_loss : 0.794979989528656 ... g_loss : 2.1908798217773438 Epoch : 2/10 ... Step : 5200 ... d_loss : 0.7883226871490479 ... g_loss : 2.3531274795532227 Epoch : 2/10 ... Step : 5300 ... d_loss : 1.1059893369674683 ... g_loss : 2.5480332374572754 Epoch : 2/10 ... Step : 5400 ... d_loss : 0.8394128680229187 ... g_loss : 2.898387908935547 Epoch : 2/10 ... Step : 5500 ... d_loss : 0.6525368690490723 ... g_loss : 1.7932838201522827 Epoch : 2/10 ... Step : 5600 ... d_loss : 0.9933221340179443 ... g_loss : 2.732285976409912 Epoch : 2/10 ... Step : 5700 ... d_loss : 1.815030574798584 ... g_loss : 0.6647165417671204 Epoch : 2/10 ... Step : 5800 ... d_loss : 0.3374061584472656 ... g_loss : 3.555860996246338 Epoch : 2/10 ... Step : 5900 ... d_loss : 1.0418013334274292 ... g_loss : 0.9163437485694885 Epoch : 2/10 ... Step : 6000 ... d_loss : 0.9274795055389404 ... g_loss : 1.5534119606018066
Epoch : 2/10 ... Step : 6100 ... d_loss : 1.0513306856155396 ... g_loss : 2.5936737060546875 Epoch : 2/10 ... Step : 6200 ... d_loss : 0.7317748069763184 ... g_loss : 3.2648637294769287 Epoch : 2/10 ... Step : 6300 ... d_loss : 0.9945372343063354 ... g_loss : 1.7116374969482422 Epoch : 3/10 ... Step : 6400 ... d_loss : 0.6944642066955566 ... g_loss : 2.5919370651245117 Epoch : 3/10 ... Step : 6500 ... d_loss : 1.3452577590942383 ... g_loss : 4.543449401855469 Epoch : 3/10 ... Step : 6600 ... d_loss : 0.6878675222396851 ... g_loss : 2.296178102493286 Epoch : 3/10 ... Step : 6700 ... d_loss : 0.813028872013092 ... g_loss : 2.1186952590942383 Epoch : 3/10 ... Step : 6800 ... d_loss : 0.7233805656433105 ... g_loss : 2.300830364227295 Epoch : 3/10 ... Step : 6900 ... d_loss : 0.7770169377326965 ... g_loss : 2.75323486328125 Epoch : 3/10 ... Step : 7000 ... d_loss : 1.0308586359024048 ... g_loss : 1.4336400032043457
Epoch : 3/10 ... Step : 7100 ... d_loss : 1.5989021062850952 ... g_loss : 0.7926545143127441 Epoch : 3/10 ... Step : 7200 ... d_loss : 0.8878282904624939 ... g_loss : 2.640322685241699 Epoch : 3/10 ... Step : 7300 ... d_loss : 0.841025173664093 ... g_loss : 1.9909579753875732 Epoch : 3/10 ... Step : 7400 ... d_loss : 0.7161791324615479 ... g_loss : 3.7084102630615234 Epoch : 3/10 ... Step : 7500 ... d_loss : 0.9519063234329224 ... g_loss : 1.9285218715667725 Epoch : 3/10 ... Step : 7600 ... d_loss : 0.6772498488426208 ... g_loss : 3.6724724769592285 Epoch : 3/10 ... Step : 7700 ... d_loss : 1.0046195983886719 ... g_loss : 1.606713056564331 Epoch : 3/10 ... Step : 7800 ... d_loss : 0.7340472936630249 ... g_loss : 2.3877339363098145 Epoch : 3/10 ... Step : 7900 ... d_loss : 0.6103564500808716 ... g_loss : 2.388050079345703 Epoch : 3/10 ... Step : 8000 ... d_loss : 1.90255606174469 ... g_loss : 4.127630710601807
Epoch : 3/10 ... Step : 8100 ... d_loss : 0.8253073692321777 ... g_loss : 1.6821367740631104 Epoch : 3/10 ... Step : 8200 ... d_loss : 1.077162504196167 ... g_loss : 2.130045175552368 Epoch : 3/10 ... Step : 8300 ... d_loss : 0.8370654582977295 ... g_loss : 1.717990517616272 Epoch : 3/10 ... Step : 8400 ... d_loss : 0.9179205894470215 ... g_loss : 3.027897357940674 Epoch : 3/10 ... Step : 8500 ... d_loss : 0.7762618660926819 ... g_loss : 3.4756534099578857 Epoch : 3/10 ... Step : 8600 ... d_loss : 0.8043072819709778 ... g_loss : 1.7576829195022583 Epoch : 3/10 ... Step : 8700 ... d_loss : 0.8062787055969238 ... g_loss : 1.5709490776062012 Epoch : 3/10 ... Step : 8800 ... d_loss : 0.7743768095970154 ... g_loss : 2.53987979888916 Epoch : 3/10 ... Step : 8900 ... d_loss : 0.5028189420700073 ... g_loss : 2.766352653503418 Epoch : 3/10 ... Step : 9000 ... d_loss : 0.9073359966278076 ... g_loss : 3.59749698638916
Epoch : 3/10 ... Step : 9100 ... d_loss : 0.7329654097557068 ... g_loss : 2.858391523361206 Epoch : 3/10 ... Step : 9200 ... d_loss : 0.6823814511299133 ... g_loss : 3.1438064575195312 Epoch : 3/10 ... Step : 9300 ... d_loss : 0.882191002368927 ... g_loss : 3.844712495803833 Epoch : 3/10 ... Step : 9400 ... d_loss : 0.4378136694431305 ... g_loss : 2.3995633125305176 Epoch : 4/10 ... Step : 9500 ... d_loss : 0.8612440228462219 ... g_loss : 2.706346273422241 Epoch : 4/10 ... Step : 9600 ... d_loss : 1.3807734251022339 ... g_loss : 1.6906564235687256 Epoch : 4/10 ... Step : 9700 ... d_loss : 1.057225227355957 ... g_loss : 1.6074702739715576 Epoch : 4/10 ... Step : 9800 ... d_loss : 0.47769269347190857 ... g_loss : 3.713808536529541 Epoch : 4/10 ... Step : 9900 ... d_loss : 1.0058733224868774 ... g_loss : 1.3367810249328613 Epoch : 4/10 ... Step : 10000 ... d_loss : 1.397682547569275 ... g_loss : 1.5888102054595947
Epoch : 4/10 ... Step : 10100 ... d_loss : 0.5938043594360352 ... g_loss : 2.1247968673706055 Epoch : 4/10 ... Step : 10200 ... d_loss : 0.48378825187683105 ... g_loss : 3.384702205657959 Epoch : 4/10 ... Step : 10300 ... d_loss : 0.6172981858253479 ... g_loss : 2.182236671447754 Epoch : 4/10 ... Step : 10400 ... d_loss : 1.9491949081420898 ... g_loss : 6.947451591491699 Epoch : 4/10 ... Step : 10500 ... d_loss : 1.39873468875885 ... g_loss : 1.5114185810089111 Epoch : 4/10 ... Step : 10600 ... d_loss : 0.48040392994880676 ... g_loss : 3.094996452331543 Epoch : 4/10 ... Step : 10700 ... d_loss : 0.431443989276886 ... g_loss : 3.181121826171875 Epoch : 4/10 ... Step : 10800 ... d_loss : 0.3543647527694702 ... g_loss : 2.7621233463287354 Epoch : 4/10 ... Step : 10900 ... d_loss : 0.7008713483810425 ... g_loss : 2.506425619125366 Epoch : 4/10 ... Step : 11800 ... d_loss : 0.8754024505615234 ... g_loss : 3.7965898513793945 Epoch : 4/10 ... Step : 11900 ... d_loss : 0.6336652040481567 ... g_loss : 2.5613436698913574 Epoch : 4/10 ... Step : 12000 ... d_loss : 1.003045916557312 ... g_loss : 2.603560447692871
Epoch : 4/10 ... Step : 12100 ... d_loss : 0.6211192011833191 ... g_loss : 4.35465145111084 Epoch : 4/10 ... Step : 12200 ... d_loss : 1.658236026763916 ... g_loss : 2.0307936668395996 Epoch : 4/10 ... Step : 12300 ... d_loss : 0.6643416285514832 ... g_loss : 1.6215614080429077 Epoch : 4/10 ... Step : 12400 ... d_loss : 1.5037124156951904 ... g_loss : 2.4061150550842285 Epoch : 4/10 ... Step : 12500 ... d_loss : 0.7234397530555725 ... g_loss : 2.240448236465454 Epoch : 4/10 ... Step : 12600 ... d_loss : 0.3444484770298004 ... g_loss : 2.8798298835754395 Epoch : 5/10 ... Step : 12700 ... d_loss : 0.7536215782165527 ... g_loss : 2.1119399070739746 Epoch : 5/10 ... Step : 12800 ... d_loss : 0.7769818305969238 ... g_loss : 2.476907968521118 Epoch : 5/10 ... Step : 12900 ... d_loss : 0.69501793384552 ... g_loss : 1.570034146308899 Epoch : 5/10 ... Step : 13000 ... d_loss : 0.6170004606246948 ... g_loss : 2.6995766162872314
Epoch : 5/10 ... Step : 13100 ... d_loss : 0.8058935403823853 ... g_loss : 2.419325351715088 Epoch : 5/10 ... Step : 13200 ... d_loss : 0.5407514572143555 ... g_loss : 2.5013070106506348 Epoch : 5/10 ... Step : 13300 ... d_loss : 0.8988519310951233 ... g_loss : 4.083812713623047 Epoch : 5/10 ... Step : 13400 ... d_loss : 0.8947582840919495 ... g_loss : 1.4938912391662598 Epoch : 5/10 ... Step : 13500 ... d_loss : 1.064157485961914 ... g_loss : 2.2391250133514404 Epoch : 5/10 ... Step : 13600 ... d_loss : 0.5946565270423889 ... g_loss : 3.9684195518493652 Epoch : 5/10 ... Step : 13700 ... d_loss : 0.43883684277534485 ... g_loss : 2.951200485229492 Epoch : 5/10 ... Step : 13800 ... d_loss : 0.6556768417358398 ... g_loss : 3.880678176879883 Epoch : 5/10 ... Step : 13900 ... d_loss : 1.002642273902893 ... g_loss : 2.3056423664093018 Epoch : 5/10 ... Step : 14000 ... d_loss : 0.6752640008926392 ... g_loss : 3.771655797958374
Epoch : 5/10 ... Step : 14100 ... d_loss : 0.8202133178710938 ... g_loss : 1.6891754865646362 Epoch : 5/10 ... Step : 14200 ... d_loss : 0.3674400746822357 ... g_loss : 4.801840782165527 Epoch : 5/10 ... Step : 14300 ... d_loss : 1.2117040157318115 ... g_loss : 2.08512544631958 Epoch : 5/10 ... Step : 14400 ... d_loss : 1.2354344129562378 ... g_loss : 4.165423393249512 Epoch : 5/10 ... Step : 14500 ... d_loss : 0.5540699362754822 ... g_loss : 2.5937650203704834 Epoch : 5/10 ... Step : 14600 ... d_loss : 1.6435577869415283 ... g_loss : 5.541172504425049 Epoch : 5/10 ... Step : 14700 ... d_loss : 0.6583627462387085 ... g_loss : 2.199554920196533 Epoch : 5/10 ... Step : 14800 ... d_loss : 0.7239829301834106 ... g_loss : 3.034944534301758 Epoch : 5/10 ... Step : 14900 ... d_loss : 0.5993967056274414 ... g_loss : 4.690726280212402 Epoch : 5/10 ... Step : 15000 ... d_loss : 0.7905285358428955 ... g_loss : 1.5383784770965576
Epoch : 5/10 ... Step : 15100 ... d_loss : 0.5516192317008972 ... g_loss : 4.919063091278076 Epoch : 5/10 ... Step : 15200 ... d_loss : 0.46049878001213074 ... g_loss : 2.7882323265075684 Epoch : 5/10 ... Step : 15300 ... d_loss : 0.40533751249313354 ... g_loss : 2.6480488777160645 Epoch : 5/10 ... Step : 15400 ... d_loss : 0.4720645546913147 ... g_loss : 3.9926233291625977 Epoch : 5/10 ... Step : 15500 ... d_loss : 0.46884599328041077 ... g_loss : 3.5910186767578125 Epoch : 5/10 ... Step : 15600 ... d_loss : 0.18347638845443726 ... g_loss : 2.7826948165893555 Epoch : 5/10 ... Step : 15700 ... d_loss : 0.7544447183609009 ... g_loss : 3.6865592002868652 Epoch : 5/10 ... Step : 15800 ... d_loss : 0.9582101702690125 ... g_loss : 4.296856880187988 Epoch : 6/10 ... Step : 15900 ... d_loss : 0.7313748002052307 ... g_loss : 4.2700300216674805 Epoch : 6/10 ... Step : 16000 ... d_loss : 1.1943225860595703 ... g_loss : 5.52423095703125
Epoch : 6/10 ... Step : 16100 ... d_loss : 0.385989785194397 ... g_loss : 3.679933547973633 Epoch : 6/10 ... Step : 16200 ... d_loss : 0.5406052470207214 ... g_loss : 2.537419557571411 Epoch : 6/10 ... Step : 16300 ... d_loss : 0.1806148886680603 ... g_loss : 4.26736307144165 Epoch : 6/10 ... Step : 16400 ... d_loss : 1.7986392974853516 ... g_loss : 0.7476070523262024 Epoch : 6/10 ... Step : 16500 ... d_loss : 0.4983261227607727 ... g_loss : 2.920379638671875 Epoch : 6/10 ... Step : 16600 ... d_loss : 0.8890631198883057 ... g_loss : 4.82114839553833 Epoch : 6/10 ... Step : 16700 ... d_loss : 1.08734929561615 ... g_loss : 4.238160610198975 Epoch : 6/10 ... Step : 16800 ... d_loss : 0.3918936252593994 ... g_loss : 3.202500820159912 Epoch : 6/10 ... Step : 16900 ... d_loss : 0.5661864876747131 ... g_loss : 2.9998488426208496 Epoch : 6/10 ... Step : 17000 ... d_loss : 0.23620469868183136 ... g_loss : 2.891629457473755
Epoch : 6/10 ... Step : 17100 ... d_loss : 1.912022352218628 ... g_loss : 6.851269721984863 Epoch : 6/10 ... Step : 17200 ... d_loss : 0.2957904636859894 ... g_loss : 4.145904064178467 Epoch : 6/10 ... Step : 17300 ... d_loss : 0.41084158420562744 ... g_loss : 3.6716904640197754 Epoch : 6/10 ... Step : 17400 ... d_loss : 0.5684086084365845 ... g_loss : 2.4906818866729736 Epoch : 6/10 ... Step : 17500 ... d_loss : 0.43952611088752747 ... g_loss : 3.215975046157837 Epoch : 6/10 ... Step : 17600 ... d_loss : 0.33327606320381165 ... g_loss : 3.0931453704833984 Epoch : 6/10 ... Step : 17700 ... d_loss : 0.8682780861854553 ... g_loss : 4.34783935546875 Epoch : 6/10 ... Step : 17800 ... d_loss : 0.2760574519634247 ... g_loss : 3.0469307899475098 Epoch : 6/10 ... Step : 17900 ... d_loss : 0.5484927296638489 ... g_loss : 4.701061248779297 Epoch : 6/10 ... Step : 18000 ... d_loss : 0.28248298168182373 ... g_loss : 4.470624923706055
Epoch : 6/10 ... Step : 18100 ... d_loss : 0.2695339024066925 ... g_loss : 3.416088342666626 Epoch : 6/10 ... Step : 18200 ... d_loss : 0.2814989984035492 ... g_loss : 3.1790480613708496 Epoch : 6/10 ... Step : 18300 ... d_loss : 0.9074666500091553 ... g_loss : 5.14849853515625 Epoch : 6/10 ... Step : 18400 ... d_loss : 0.30773961544036865 ... g_loss : 4.046170711517334 Epoch : 6/10 ... Step : 18500 ... d_loss : 0.30610859394073486 ... g_loss : 4.413356781005859 Epoch : 6/10 ... Step : 18600 ... d_loss : 1.2482481002807617 ... g_loss : 2.0637271404266357 Epoch : 6/10 ... Step : 18700 ... d_loss : 0.39115846157073975 ... g_loss : 3.5438990592956543 Epoch : 6/10 ... Step : 18800 ... d_loss : 0.6940707564353943 ... g_loss : 2.3031558990478516 Epoch : 6/10 ... Step : 18900 ... d_loss : 0.21500104665756226 ... g_loss : 4.14189338684082 Epoch : 7/10 ... Step : 19000 ... d_loss : 0.3661699891090393 ... g_loss : 4.3759660720825195
Epoch : 7/10 ... Step : 19100 ... d_loss : 0.7071528434753418 ... g_loss : 3.3849949836730957 Epoch : 7/10 ... Step : 19200 ... d_loss : 0.27790147066116333 ... g_loss : 3.8397059440612793 Epoch : 7/10 ... Step : 19300 ... d_loss : 0.38583359122276306 ... g_loss : 4.359880447387695 Epoch : 7/10 ... Step : 19400 ... d_loss : 1.1872444152832031 ... g_loss : 1.3505476713180542 Epoch : 7/10 ... Step : 19500 ... d_loss : 0.52000492811203 ... g_loss : 3.4369068145751953 Epoch : 7/10 ... Step : 19600 ... d_loss : 0.3557535409927368 ... g_loss : 3.2199294567108154 Epoch : 7/10 ... Step : 19700 ... d_loss : 0.2904711961746216 ... g_loss : 5.15902042388916 Epoch : 7/10 ... Step : 19800 ... d_loss : 1.636960506439209 ... g_loss : 8.877716064453125 Epoch : 7/10 ... Step : 19900 ... d_loss : 2.220370292663574 ... g_loss : 1.9596445560455322 Epoch : 7/10 ... Step : 20000 ... d_loss : 0.5225305557250977 ... g_loss : 3.0324621200561523
Epoch : 7/10 ... Step : 20100 ... d_loss : 0.7955620288848877 ... g_loss : 1.3638322353363037 Epoch : 7/10 ... Step : 20200 ... d_loss : 0.3061924874782562 ... g_loss : 4.405516624450684 Epoch : 7/10 ... Step : 20300 ... d_loss : 0.3211655914783478 ... g_loss : 4.232878684997559 Epoch : 7/10 ... Step : 20400 ... d_loss : 1.2649412155151367 ... g_loss : 1.2986468076705933 Epoch : 7/10 ... Step : 20500 ... d_loss : 0.5475189089775085 ... g_loss : 3.294623374938965 Epoch : 7/10 ... Step : 20600 ... d_loss : 0.4550027549266815 ... g_loss : 4.140321254730225 Epoch : 7/10 ... Step : 20700 ... d_loss : 0.7625323534011841 ... g_loss : 3.5628609657287598 Epoch : 7/10 ... Step : 20800 ... d_loss : 0.2218743860721588 ... g_loss : 3.974705457687378 Epoch : 7/10 ... Step : 20900 ... d_loss : 0.3717931807041168 ... g_loss : 4.583436012268066 Epoch : 7/10 ... Step : 21000 ... d_loss : 0.8192095756530762 ... g_loss : 2.687600612640381
Epoch : 7/10 ... Step : 21100 ... d_loss : 0.623580813407898 ... g_loss : 4.868472099304199 Epoch : 7/10 ... Step : 21200 ... d_loss : 1.0972751379013062 ... g_loss : 1.2435379028320312 Epoch : 7/10 ... Step : 21300 ... d_loss : 1.1412360668182373 ... g_loss : 2.9949889183044434 Epoch : 7/10 ... Step : 21400 ... d_loss : 0.5461686849594116 ... g_loss : 3.1392970085144043 Epoch : 7/10 ... Step : 21500 ... d_loss : 0.38025012612342834 ... g_loss : 3.3059778213500977 Epoch : 7/10 ... Step : 21600 ... d_loss : 1.2774477005004883 ... g_loss : 1.0083229541778564 Epoch : 7/10 ... Step : 21700 ... d_loss : 0.4087792634963989 ... g_loss : 4.880535125732422 Epoch : 7/10 ... Step : 21800 ... d_loss : 0.7504922747612 ... g_loss : 4.991997718811035 Epoch : 7/10 ... Step : 21900 ... d_loss : 3.14534068107605 ... g_loss : 0.9757068157196045 Epoch : 7/10 ... Step : 22000 ... d_loss : 0.5753359198570251 ... g_loss : 3.2147979736328125
Epoch : 7/10 ... Step : 22100 ... d_loss : 1.4733750820159912 ... g_loss : 4.149021625518799 Epoch : 8/10 ... Step : 22200 ... d_loss : 0.25395452976226807 ... g_loss : 3.4134278297424316 Epoch : 8/10 ... Step : 22300 ... d_loss : 0.37968260049819946 ... g_loss : 3.0457379817962646 Epoch : 8/10 ... Step : 22400 ... d_loss : 0.5422947406768799 ... g_loss : 3.911689281463623 Epoch : 8/10 ... Step : 22500 ... d_loss : 0.4606148898601532 ... g_loss : 4.249947547912598 Epoch : 8/10 ... Step : 22600 ... d_loss : 0.18404912948608398 ... g_loss : 4.14982271194458 Epoch : 8/10 ... Step : 22700 ... d_loss : 0.5949870944023132 ... g_loss : 4.7815423011779785 Epoch : 8/10 ... Step : 22800 ... d_loss : 0.4282451868057251 ... g_loss : 2.850914239883423 Epoch : 8/10 ... Step : 22900 ... d_loss : 0.5931434035301208 ... g_loss : 5.224471569061279 Epoch : 8/10 ... Step : 23000 ... d_loss : 0.3509417474269867 ... g_loss : 4.103386402130127
Epoch : 8/10 ... Step : 23100 ... d_loss : 0.4083268344402313 ... g_loss : 4.703277587890625 Epoch : 8/10 ... Step : 23200 ... d_loss : 0.7648941874504089 ... g_loss : 2.159581422805786 Epoch : 8/10 ... Step : 23300 ... d_loss : 0.7974314093589783 ... g_loss : 4.739001274108887 Epoch : 8/10 ... Step : 23400 ... d_loss : 0.39900290966033936 ... g_loss : 4.106310844421387 Epoch : 8/10 ... Step : 23500 ... d_loss : 0.5239807367324829 ... g_loss : 3.415222644805908 Epoch : 8/10 ... Step : 23600 ... d_loss : 0.2796063721179962 ... g_loss : 3.1567869186401367 Epoch : 8/10 ... Step : 23700 ... d_loss : 0.3090367913246155 ... g_loss : 3.54063081741333 Epoch : 8/10 ... Step : 23800 ... d_loss : 0.5435348749160767 ... g_loss : 3.941195487976074 Epoch : 8/10 ... Step : 23900 ... d_loss : 0.4103485345840454 ... g_loss : 4.36937141418457 Epoch : 8/10 ... Step : 24000 ... d_loss : 0.5792438387870789 ... g_loss : 2.911807060241699
Epoch : 8/10 ... Step : 24100 ... d_loss : 0.30811288952827454 ... g_loss : 4.566879749298096 Epoch : 8/10 ... Step : 24200 ... d_loss : 0.7083595991134644 ... g_loss : 2.1919918060302734 Epoch : 8/10 ... Step : 24300 ... d_loss : 0.5555270314216614 ... g_loss : 3.1047685146331787 Epoch : 8/10 ... Step : 24400 ... d_loss : 0.7927849292755127 ... g_loss : 2.3599026203155518 Epoch : 8/10 ... Step : 24500 ... d_loss : 1.3078144788742065 ... g_loss : 6.797451972961426 Epoch : 8/10 ... Step : 24600 ... d_loss : 0.5142228007316589 ... g_loss : 4.588727951049805 Epoch : 8/10 ... Step : 24700 ... d_loss : 0.3275877833366394 ... g_loss : 3.71189284324646 Epoch : 8/10 ... Step : 24800 ... d_loss : 0.501152753829956 ... g_loss : 4.264185428619385 Epoch : 8/10 ... Step : 24900 ... d_loss : 0.20144537091255188 ... g_loss : 3.9159388542175293 Epoch : 8/10 ... Step : 25000 ... d_loss : 0.4248126745223999 ... g_loss : 3.785752296447754
Epoch : 8/10 ... Step : 25100 ... d_loss : 0.4099251329898834 ... g_loss : 2.280766487121582 Epoch : 8/10 ... Step : 25200 ... d_loss : 0.9084678292274475 ... g_loss : 3.91361927986145 Epoch : 8/10 ... Step : 25300 ... d_loss : 1.0113188028335571 ... g_loss : 2.507631301879883 Epoch : 9/10 ... Step : 25400 ... d_loss : 0.36031487584114075 ... g_loss : 5.1182050704956055 Epoch : 9/10 ... Step : 25500 ... d_loss : 0.05344564467668533 ... g_loss : 7.011328220367432 Epoch : 9/10 ... Step : 25600 ... d_loss : 0.3123284578323364 ... g_loss : 4.889743328094482 Epoch : 9/10 ... Step : 25700 ... d_loss : 0.533519446849823 ... g_loss : 4.860894203186035 Epoch : 9/10 ... Step : 25800 ... d_loss : 0.08331054449081421 ... g_loss : 4.677060127258301 Epoch : 9/10 ... Step : 25900 ... d_loss : 0.6761173009872437 ... g_loss : 2.1354076862335205 Epoch : 9/10 ... Step : 26000 ... d_loss : 0.16820071637630463 ... g_loss : 3.905470132827759
Epoch : 9/10 ... Step : 26100 ... d_loss : 0.29596367478370667 ... g_loss : 4.333805561065674 Epoch : 9/10 ... Step : 26200 ... d_loss : 0.43651095032691956 ... g_loss : 4.021414756774902 Epoch : 9/10 ... Step : 26300 ... d_loss : 0.5232959985733032 ... g_loss : 2.571570873260498 Epoch : 9/10 ... Step : 26400 ... d_loss : 0.9275932908058167 ... g_loss : 2.4451475143432617 Epoch : 9/10 ... Step : 26500 ... d_loss : 0.732362687587738 ... g_loss : 2.0951693058013916 Epoch : 9/10 ... Step : 26600 ... d_loss : 0.5919581651687622 ... g_loss : 1.7505412101745605 Epoch : 9/10 ... Step : 26700 ... d_loss : 1.2631993293762207 ... g_loss : 6.563183784484863 Epoch : 9/10 ... Step : 26800 ... d_loss : 0.5323549509048462 ... g_loss : 3.322566509246826 Epoch : 9/10 ... Step : 26900 ... d_loss : 0.21753285825252533 ... g_loss : 4.194251537322998 Epoch : 9/10 ... Step : 27000 ... d_loss : 0.5011434555053711 ... g_loss : 5.234610080718994
Epoch : 9/10 ... Step : 27100 ... d_loss : 0.6940739154815674 ... g_loss : 1.6781619787216187 Epoch : 9/10 ... Step : 27200 ... d_loss : 0.4003569483757019 ... g_loss : 3.2724404335021973 Epoch : 9/10 ... Step : 27300 ... d_loss : 0.10880632698535919 ... g_loss : 6.0382161140441895 Epoch : 9/10 ... Step : 27400 ... d_loss : 0.4500623941421509 ... g_loss : 4.10192346572876 Epoch : 9/10 ... Step : 27500 ... d_loss : 0.3227854073047638 ... g_loss : 4.521537780761719 Epoch : 9/10 ... Step : 27600 ... d_loss : 0.30751892924308777 ... g_loss : 3.2214925289154053 Epoch : 9/10 ... Step : 27700 ... d_loss : 0.812039852142334 ... g_loss : 5.79445743560791 Epoch : 9/10 ... Step : 27800 ... d_loss : 0.3711985647678375 ... g_loss : 3.206743001937866 Epoch : 9/10 ... Step : 27900 ... d_loss : 0.282046914100647 ... g_loss : 4.102575302124023 Epoch : 9/10 ... Step : 28000 ... d_loss : 0.5198266506195068 ... g_loss : 4.311180114746094
Epoch : 9/10 ... Step : 28100 ... d_loss : 0.7660457491874695 ... g_loss : 6.059112548828125 Epoch : 9/10 ... Step : 28200 ... d_loss : 1.2874704599380493 ... g_loss : 2.0908660888671875 Epoch : 9/10 ... Step : 28300 ... d_loss : 0.5435035824775696 ... g_loss : 3.196681499481201 Epoch : 9/10 ... Step : 28400 ... d_loss : 0.2556459307670593 ... g_loss : 5.736785888671875 Epoch : 10/10 ... Step : 28500 ... d_loss : 0.378069669008255 ... g_loss : 4.2180352210998535 Epoch : 10/10 ... Step : 28600 ... d_loss : 0.6158527135848999 ... g_loss : 4.70815372467041 Epoch : 10/10 ... Step : 28700 ... d_loss : 0.0896947979927063 ... g_loss : 4.471714973449707 Epoch : 10/10 ... Step : 28800 ... d_loss : 0.24882569909095764 ... g_loss : 3.7529296875 Epoch : 10/10 ... Step : 28900 ... d_loss : 0.8011612296104431 ... g_loss : 1.6903131008148193 Epoch : 10/10 ... Step : 29000 ... d_loss : 0.5015291571617126 ... g_loss : 3.4335780143737793
Epoch : 10/10 ... Step : 29100 ... d_loss : 0.4106074571609497 ... g_loss : 3.4274582862854004 Epoch : 10/10 ... Step : 29200 ... d_loss : 2.248307228088379 ... g_loss : 8.01930046081543 Epoch : 10/10 ... Step : 29300 ... d_loss : 0.2778056263923645 ... g_loss : 3.558537006378174 Epoch : 10/10 ... Step : 29400 ... d_loss : 0.3079754710197449 ... g_loss : 3.8532679080963135 Epoch : 10/10 ... Step : 29500 ... d_loss : 0.43863198161125183 ... g_loss : 4.669306755065918 Epoch : 10/10 ... Step : 29600 ... d_loss : 1.680667757987976 ... g_loss : 5.894677639007568 Epoch : 10/10 ... Step : 29700 ... d_loss : 0.33130979537963867 ... g_loss : 3.553741455078125 Epoch : 10/10 ... Step : 29800 ... d_loss : 0.18311983346939087 ... g_loss : 4.499373912811279 Epoch : 10/10 ... Step : 29900 ... d_loss : 0.5762640237808228 ... g_loss : 4.349750518798828 Epoch : 10/10 ... Step : 30000 ... d_loss : 0.17546240985393524 ... g_loss : 5.212212562561035
Epoch : 10/10 ... Step : 30100 ... d_loss : 0.10799669474363327 ... g_loss : 4.463862419128418 Epoch : 10/10 ... Step : 30200 ... d_loss : 0.5007672309875488 ... g_loss : 3.6731388568878174 Epoch : 10/10 ... Step : 30300 ... d_loss : 0.2675006687641144 ... g_loss : 4.114383697509766 Epoch : 10/10 ... Step : 30400 ... d_loss : 0.2227734923362732 ... g_loss : 4.130051612854004 Epoch : 10/10 ... Step : 30500 ... d_loss : 0.28389307856559753 ... g_loss : 4.81640625 Epoch : 10/10 ... Step : 30600 ... d_loss : 0.5074851512908936 ... g_loss : 3.6572933197021484 Epoch : 10/10 ... Step : 30700 ... d_loss : 0.6775749325752258 ... g_loss : 2.3551504611968994 Epoch : 10/10 ... Step : 30800 ... d_loss : 0.5641292333602905 ... g_loss : 4.442119598388672 Epoch : 10/10 ... Step : 30900 ... d_loss : 1.1315271854400635 ... g_loss : 1.7341861724853516 Epoch : 10/10 ... Step : 31000 ... d_loss : 0.2348896563053131 ... g_loss : 4.009011268615723
Epoch : 10/10 ... Step : 31100 ... d_loss : 0.9523839950561523 ... g_loss : 6.449118614196777 Epoch : 10/10 ... Step : 31200 ... d_loss : 0.3498571515083313 ... g_loss : 3.347778797149658 Epoch : 10/10 ... Step : 31300 ... d_loss : 0.39960625767707825 ... g_loss : 4.0286054611206055 Epoch : 10/10 ... Step : 31400 ... d_loss : 0.11098617315292358 ... g_loss : 4.664855003356934 Epoch : 10/10 ... Step : 31500 ... d_loss : 0.20356731116771698 ... g_loss : 4.337280750274658 Epoch : 10/10 ... Step : 31600 ... d_loss : 0.5650681257247925 ... g_loss : 2.6981019973754883
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.